
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler


# Set additional parameters for charts and tables

# Remove column display limits
pd.set_option('display.max_columns', None)
# pd.set_option('display.height', None)
pd.set_option('display.max_rows', None)
# pd.set_option('display.width', None)
plt.rcParams['figure.figsize'] = [15, 10]
sns.set_style("white")


# Read data in csv format, using encoding, and read the 3rd row as column names

chile_data1 = pd.read_csv('Tourism Chile D1 - D5_updated1019.csv', encoding = 'ISO-8859-1', header = 3)
chile_data1


# Remove first 2 columns
chile_data1.drop(chile_data1.iloc[:, 0:2], inplace = True, axis = 1)


# Remove last row with the aggregated total data
chile_data1 = chile_data1[:-1]


# Rename the first column from "VARIABLE" to "Regions"
chile_data1 = chile_data1.rename(columns={'VARIABLE': 'Regions'})


chile_data1


# Check datatype of columns: integers or floats, or objects.
chile_data1.dtypes

Regions                                                                       object
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                float64
NUMBER OF CULTURAL CENTERS                                                     int64
WORLD CULTURAL HERITAGE SITES                                                  int64
NUMBER OF ARCHEOLOGICAL SITES                                                  int64
NATIONAL MONUMENTS                                                             int64
MUSEUMS                                                                        int64
% OF POPULATION THAT ATTENDS MUSEUMS                                         float64
THEATERS                                                                       int64
NUMBER OF THEATER PLAYS PER YEAR                                               int64
LIBRARIES                                                                      int64
GALERIES                                                                       int64
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            float64
NUMBER OF EXHIBITS                                                             int64
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR              int64
MAJOR SPORTS EVENTS PER YEAR                                                   int64
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                              int64
ARTWORK SITES                                                                  int64
POPULAR ARCHITECTURE SITES                                                     int64
HISTORICAL SITES                                                               int64
LOCAL MARKETS                                                                  int64
CULTURAL SITES LEVEL III (INTERNATIONAL)                                       int64
CULTURA SITES LEVEL II (NATIONAL)                                              int64
CULTURAL SITES LEVEL I (LOCAL)                                                 int64
HERITAGE ARCHITECTURAL HOUSES                                                  int64
% OF LAND THAT CORRESPONDS TO FORESTS                                         object
NATIONAL PROTECTED SITES (%)                                                 float64
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 float64
TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         float64
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                   int64
ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   float64
NUMBER OF BEACHES AND BEACH RESORTS                                            int64
LAND AFFECTED BY WILDFIRES                                                    object
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                              int64
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                    int64
RIVERS, LAKES AND WATERFALLS                                                   int64
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                      int64
GEISERS AND THERMAL CENTERS                                                    int64
PIERS AND SEASHORES                                                            int64
GLACIERS AND WINTER VACATION LOCATIONS                                         int64
VALLEYS                                                                        int64
DESERTS AND DUNES                                                              int64
ISLANDS AND PENINSULAS                                                         int64
PALEONTOLOGY SITES                                                             int64
HIKING TRAILS                                                                  int64
PRESERVED SITES                                                               object
SEASHORE PROTECTED SITES                                                       int64
BIOSHPERE RESERVES                                                             int64
% AVAILABLE WORKFORCE                                                        float64
% POPULATION ORIENTED TOWARDS TOURISM                                        float64
AVERAGE MONTHLY INCOME (CHILEAN PESOS)                                        object
5 POPULATION WITH PRIMARY EDUCATION                                          float64
% POPULATION WITH SECONDARY EDUCATION                                        float64
AVERAGE NUMBER OF YEARS STUDYING                                             float64
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  float64
TOURISM-ORIENTED INSTITUTIONS                                                  int64
NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS                        object
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                        int64
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            float64
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                      int64
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     float64
ROOMS PER 1000 HABITANTS                                                     float64
NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.                        object
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             float64
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   float64
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 float64
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                  int64
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS             int64
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    float64
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)                   int64
NATIONAL TOURISTS ARRIVALS                                                    object
INTERNATIONAL TOURISTS ARRIVALS                                               object
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                                 object
DENSITY OF AIRPORTS                                                          float64
DENSITY OF ROADS AND HIGHWAYS                                                float64
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                    float64
NUMBER OF VEHICLES                                                            object
VISITORS TO PROTECTED SITES                                                   object
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                         int64
TOURIST'S ARRIVALS THROUGH BORDER LINES                                       object
SECONDARY ROADS (KMS)                                                        float64
NUMBER OF INTERNATIONAL BORDER GATES                                           int64
dtype: object


# Define the function to clean columns that are object type

def clean_object(df):
    df = df.replace(r'[<$]', '', regex = True)       # Remove $ symbol
    df = df.replace(',','', regex = True)            # Remove commas from numbers
    df = df.replace('-','', regex = True)            # Remove `-` character
    df = df.replace(r'^\s*$', np.nan, regex = True)  # Replace empty values with NaNs
    return df


# Apply above defined function to the dataframe

chile_data1 = clean_object(chile_data1)

# Check new dataframe
# chile_data1


# Define the function to transform object columns to numeric columns

def object_to_numeric(df):
    all_columns = df.columns                                                      # Select all columns
    numerical_columns = df._get_numeric_data().columns                            # Pick out numeric columns
    object_columns = [col for col in all_columns if col not in numerical_columns] # Pick out object columns
    object_columns = object_columns[1:]                                           # Take out 'Regions' from object_columns
    df[object_columns] = df[object_columns].apply(pd.to_numeric, errors = 'coerce', axis = 1)
                                                                                  # Convert
    print(df.dtypes)                                                              # Check datatypes after transformation
    return df


# Apply above defined function to the dataframe

chile_data1 = object_to_numeric(chile_data1)

Regions                                                                       object
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                float64
NUMBER OF CULTURAL CENTERS                                                     int64
WORLD CULTURAL HERITAGE SITES                                                  int64
NUMBER OF ARCHEOLOGICAL SITES                                                  int64
NATIONAL MONUMENTS                                                             int64
MUSEUMS                                                                        int64
% OF POPULATION THAT ATTENDS MUSEUMS                                         float64
THEATERS                                                                       int64
NUMBER OF THEATER PLAYS PER YEAR                                               int64
LIBRARIES                                                                      int64
GALERIES                                                                       int64
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            float64
NUMBER OF EXHIBITS                                                             int64
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR              int64
MAJOR SPORTS EVENTS PER YEAR                                                   int64
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                              int64
ARTWORK SITES                                                                  int64
POPULAR ARCHITECTURE SITES                                                     int64
HISTORICAL SITES                                                               int64
LOCAL MARKETS                                                                  int64
CULTURAL SITES LEVEL III (INTERNATIONAL)                                       int64
CULTURA SITES LEVEL II (NATIONAL)                                              int64
CULTURAL SITES LEVEL I (LOCAL)                                                 int64
HERITAGE ARCHITECTURAL HOUSES                                                  int64
% OF LAND THAT CORRESPONDS TO FORESTS                                        float64
NATIONAL PROTECTED SITES (%)                                                 float64
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 float64
TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         float64
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                   int64
ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   float64
NUMBER OF BEACHES AND BEACH RESORTS                                            int64
LAND AFFECTED BY WILDFIRES                                                   float64
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                              int64
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                    int64
RIVERS, LAKES AND WATERFALLS                                                   int64
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                      int64
GEISERS AND THERMAL CENTERS                                                    int64
PIERS AND SEASHORES                                                            int64
GLACIERS AND WINTER VACATION LOCATIONS                                         int64
VALLEYS                                                                        int64
DESERTS AND DUNES                                                              int64
ISLANDS AND PENINSULAS                                                         int64
PALEONTOLOGY SITES                                                             int64
HIKING TRAILS                                                                  int64
PRESERVED SITES                                                              float64
SEASHORE PROTECTED SITES                                                       int64
BIOSHPERE RESERVES                                                             int64
% AVAILABLE WORKFORCE                                                        float64
% POPULATION ORIENTED TOWARDS TOURISM                                        float64
AVERAGE MONTHLY INCOME (CHILEAN PESOS)                                       float64
5 POPULATION WITH PRIMARY EDUCATION                                          float64
% POPULATION WITH SECONDARY EDUCATION                                        float64
AVERAGE NUMBER OF YEARS STUDYING                                             float64
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  float64
TOURISM-ORIENTED INSTITUTIONS                                                  int64
NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS                       float64
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                        int64
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            float64
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                      int64
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     float64
ROOMS PER 1000 HABITANTS                                                     float64
NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.                       float64
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             float64
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   float64
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 float64
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                  int64
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS             int64
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    float64
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)                   int64
NATIONAL TOURISTS ARRIVALS                                                   float64
INTERNATIONAL TOURISTS ARRIVALS                                              float64
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                                float64
DENSITY OF AIRPORTS                                                          float64
DENSITY OF ROADS AND HIGHWAYS                                                float64
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                    float64
NUMBER OF VEHICLES                                                           float64
VISITORS TO PROTECTED SITES                                                  float64
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                         int64
TOURIST'S ARRIVALS THROUGH BORDER LINES                                      float64
SECONDARY ROADS (KMS)                                                        float64
NUMBER OF INTERNATIONAL BORDER GATES                                           int64
dtype: object


# Check NaNs in the dataset

chile_data1.isnull().sum(axis = 0)

Regions                                                                      0
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                0
NUMBER OF CULTURAL CENTERS                                                   0
WORLD CULTURAL HERITAGE SITES                                                0
NUMBER OF ARCHEOLOGICAL SITES                                                0
NATIONAL MONUMENTS                                                           0
MUSEUMS                                                                      0
% OF POPULATION THAT ATTENDS MUSEUMS                                         0
THEATERS                                                                     0
NUMBER OF THEATER PLAYS PER YEAR                                             0
LIBRARIES                                                                    0
GALERIES                                                                     0
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0
NUMBER OF EXHIBITS                                                           0
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR            0
MAJOR SPORTS EVENTS PER YEAR                                                 0
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0
ARTWORK SITES                                                                0
POPULAR ARCHITECTURE SITES                                                   0
HISTORICAL SITES                                                             0
LOCAL MARKETS                                                                0
CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0
CULTURA SITES LEVEL II (NATIONAL)                                            0
CULTURAL SITES LEVEL I (LOCAL)                                               0
HERITAGE ARCHITECTURAL HOUSES                                                0
% OF LAND THAT CORRESPONDS TO FORESTS                                        3
NATIONAL PROTECTED SITES (%)                                                 0
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0
TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         0
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0
ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   0
NUMBER OF BEACHES AND BEACH RESORTS                                          0
LAND AFFECTED BY WILDFIRES                                                   4
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0
RIVERS, LAKES AND WATERFALLS                                                 0
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                    0
GEISERS AND THERMAL CENTERS                                                  0
PIERS AND SEASHORES                                                          0
GLACIERS AND WINTER VACATION LOCATIONS                                       0
VALLEYS                                                                      0
DESERTS AND DUNES                                                            0
ISLANDS AND PENINSULAS                                                       0
PALEONTOLOGY SITES                                                           0
HIKING TRAILS                                                                0
PRESERVED SITES                                                              2
SEASHORE PROTECTED SITES                                                     0
BIOSHPERE RESERVES                                                           0
% AVAILABLE WORKFORCE                                                        0
% POPULATION ORIENTED TOWARDS TOURISM                                        0
AVERAGE MONTHLY INCOME (CHILEAN PESOS)                                       0
5 POPULATION WITH PRIMARY EDUCATION                                          0
% POPULATION WITH SECONDARY EDUCATION                                        0
AVERAGE NUMBER OF YEARS STUDYING                                             0
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0
TOURISM-ORIENTED INSTITUTIONS                                                0
NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS                       0
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     0
ROOMS PER 1000 HABITANTS                                                     0
NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.                       0
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             0
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   0
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 0
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                0
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS           0
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    0
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)                 0
NATIONAL TOURISTS ARRIVALS                                                   0
INTERNATIONAL TOURISTS ARRIVALS                                              0
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                                1
DENSITY OF AIRPORTS                                                          0
DENSITY OF ROADS AND HIGHWAYS                                                0
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                    0
NUMBER OF VEHICLES                                                           0
VISITORS TO PROTECTED SITES                                                  0
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                       0
TOURIST'S ARRIVALS THROUGH BORDER LINES                                      0
SECONDARY ROADS (KMS)                                                        0
NUMBER OF INTERNATIONAL BORDER GATES                                         0
dtype: int64


# Impute data for NaNs - strategy 1: replace with 0 (assumption explained above)

chile_data1['% OF LAND THAT CORRESPONDS TO FORESTS'] = chile_data1['% OF LAND THAT CORRESPONDS TO FORESTS'].fillna(0)
chile_data1['LAND AFFECTED BY WILDFIRES'] = chile_data1['LAND AFFECTED BY WILDFIRES'].fillna(0)


# Impute data for NaNs - strategy 2: replace with mean (assumption explained above)

imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')

chile_data1[['PRESERVED SITES', 
             'NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY']] = imputer.fit_transform(
    chile_data1[['PRESERVED SITES',
                 'NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY']])


# Check NaNs in the dataset again to make sure

chile_data1.isnull().sum(axis = 0)
# Now we don't have any NaNs

Regions                                                                      0
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                0
NUMBER OF CULTURAL CENTERS                                                   0
WORLD CULTURAL HERITAGE SITES                                                0
NUMBER OF ARCHEOLOGICAL SITES                                                0
NATIONAL MONUMENTS                                                           0
MUSEUMS                                                                      0
% OF POPULATION THAT ATTENDS MUSEUMS                                         0
THEATERS                                                                     0
NUMBER OF THEATER PLAYS PER YEAR                                             0
LIBRARIES                                                                    0
GALERIES                                                                     0
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0
NUMBER OF EXHIBITS                                                           0
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR            0
MAJOR SPORTS EVENTS PER YEAR                                                 0
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0
ARTWORK SITES                                                                0
POPULAR ARCHITECTURE SITES                                                   0
HISTORICAL SITES                                                             0
LOCAL MARKETS                                                                0
CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0
CULTURA SITES LEVEL II (NATIONAL)                                            0
CULTURAL SITES LEVEL I (LOCAL)                                               0
HERITAGE ARCHITECTURAL HOUSES                                                0
% OF LAND THAT CORRESPONDS TO FORESTS                                        0
NATIONAL PROTECTED SITES (%)                                                 0
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0
TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         0
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0
ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   0
NUMBER OF BEACHES AND BEACH RESORTS                                          0
LAND AFFECTED BY WILDFIRES                                                   0
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0
RIVERS, LAKES AND WATERFALLS                                                 0
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                    0
GEISERS AND THERMAL CENTERS                                                  0
PIERS AND SEASHORES                                                          0
GLACIERS AND WINTER VACATION LOCATIONS                                       0
VALLEYS                                                                      0
DESERTS AND DUNES                                                            0
ISLANDS AND PENINSULAS                                                       0
PALEONTOLOGY SITES                                                           0
HIKING TRAILS                                                                0
PRESERVED SITES                                                              0
SEASHORE PROTECTED SITES                                                     0
BIOSHPERE RESERVES                                                           0
% AVAILABLE WORKFORCE                                                        0
% POPULATION ORIENTED TOWARDS TOURISM                                        0
AVERAGE MONTHLY INCOME (CHILEAN PESOS)                                       0
5 POPULATION WITH PRIMARY EDUCATION                                          0
% POPULATION WITH SECONDARY EDUCATION                                        0
AVERAGE NUMBER OF YEARS STUDYING                                             0
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0
TOURISM-ORIENTED INSTITUTIONS                                                0
NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS                       0
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     0
ROOMS PER 1000 HABITANTS                                                     0
NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.                       0
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             0
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   0
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 0
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                0
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS           0
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    0
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)                 0
NATIONAL TOURISTS ARRIVALS                                                   0
INTERNATIONAL TOURISTS ARRIVALS                                              0
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                                0
DENSITY OF AIRPORTS                                                          0
DENSITY OF ROADS AND HIGHWAYS                                                0
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                    0
NUMBER OF VEHICLES                                                           0
VISITORS TO PROTECTED SITES                                                  0
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                       0
TOURIST'S ARRIVALS THROUGH BORDER LINES                                      0
SECONDARY ROADS (KMS)                                                        0
NUMBER OF INTERNATIONAL BORDER GATES                                         0
dtype: int64


# Read data in csv format, using encoding, and read the 7th row as column names

chile_data2 = pd.read_csv('Tourism Chile D6 - D10 (3_updated1019).csv', encoding = 'ISO-8859-1', header = 7)
chile_data2


# Remove last 4 rows
chile_data2 = chile_data2[:-4]

# Remove last 1 column
chile_data2 = chile_data2.drop(columns = ['Unnamed: 51'])

# Check the dataframe again
chile_data2


# Apply the function to clean columns that are object type
chile_data2 = clean_object(chile_data2)

# Check new dataframe
# chile_data2


# Apply the function to transform object columns to numeric columns

chile_data2 = object_to_numeric(chile_data2)

Regions                                                                            object
Density of restaurants per 100,000 habitants                                      float64
People working at restaurants per 10,000 habitants                                float64
Car rental agencies                                                               float64
Hospital beds per 10,000 habitants                                                float64
Density of ATM machines per 100,000 habitants                                     float64
Spas                                                                              float64
Casinos per 100,000 habitants                                                     float64
Golf courses                                                                      float64
Artwork Centers                                                                   float64
Density of tourist guides per 100,000 habitants                                   float64
Thermal centers                                                                   float64
Density of sports centers per 10,000 habitants                                    float64
Ski resorts                                                                       float64
Major shopping centers                                                            float64
Phone service penetration pero 1,000 habitants                                    float64
Density of service stations                                                       float64
Number of certified tourism-operators                                             float64
Number of vineyards                                                               float64
Percentage of crime perception                                                    float64
Percentage of homes with at least one crime victim per year                       float64
Density of murders per one million habitants                                      float64
Density of public health complaints per one million habitants                     float64
Crime index                                                                       float64
Allocated budget for public safety (millions of chilean pesos)                    float64
Porcentage of homes that reported at least one crime                              float64
Number of reported crimes                                                         float64
Number of crimes investigated and resolved                                        float64
Number of accidents (roads; air; water)                                           float64
Illegal commerce                                                                  float64
Number of police units                                                            float64
Unemployment rate                                                                 float64
Poverty rate                                                                      float64
Industrial and Intellectual property crimes                                       float64
Yearly strikes                                                                    float64
Average length of strikes                                                         float64
Estimated daily cost per individual participating on a strike                     float64
Banks per one million habitants                                                   float64
Temporary residents                                                               float64
Exports (volume in million chilean pesos)                                         float64
Density of tourism offices per one million habitants                              float64
Number of tourism information offices                                             float64
Average number of internet searches for main tourist attractions                  float64
Yearly budget for tourism promotion ($M)                                          float64
Yearly budget for international tourism promotion ($M)                            float64
Governmental resources allocated to tourism promotion ($M)                        float64
Public infrastructure investments related to the tourism sector ($)               float64
Entrepreneurial and Innovation funds allocated to tourism related projects ($)    float64
Seed funds allocated to the tourism sector ($)                                    float64
Governmental funds devoted to tourism development ($)                             float64
Number of regional strategy plans for tourism development                         float64
dtype: object


# Check NaNs in the dataset

chile_data2.isnull().sum(axis = 0)

Regions                                                                            0
Density of restaurants per 100,000 habitants                                       0
People working at restaurants per 10,000 habitants                                 0
Car rental agencies                                                                0
Hospital beds per 10,000 habitants                                                 0
Density of ATM machines per 100,000 habitants                                      0
Spas                                                                               0
Casinos per 100,000 habitants                                                      0
Golf courses                                                                       0
Artwork Centers                                                                    0
Density of tourist guides per 100,000 habitants                                    0
Thermal centers                                                                    0
Density of sports centers per 10,000 habitants                                     0
Ski resorts                                                                        6
Major shopping centers                                                             4
Phone service penetration pero 1,000 habitants                                     0
Density of service stations                                                        0
Number of certified tourism-operators                                              0
Number of vineyards                                                               11
Percentage of crime perception                                                     0
Percentage of homes with at least one crime victim per year                        0
Density of murders per one million habitants                                       0
Density of public health complaints per one million habitants                      0
Crime index                                                                        1
Allocated budget for public safety (millions of chilean pesos)                     0
Porcentage of homes that reported at least one crime                               0
Number of reported crimes                                                          0
Number of crimes investigated and resolved                                         0
Number of accidents (roads; air; water)                                            0
Illegal commerce                                                                   2
Number of police units                                                             0
Unemployment rate                                                                  0
Poverty rate                                                                       0
Industrial and Intellectual property crimes                                        0
Yearly strikes                                                                     0
Average length of strikes                                                          0
Estimated daily cost per individual participating on a strike                      0
Banks per one million habitants                                                    0
Temporary residents                                                                0
Exports (volume in million chilean pesos)                                          0
Density of tourism offices per one million habitants                               0
Number of tourism information offices                                              0
Average number of internet searches for main tourist attractions                   0
Yearly budget for tourism promotion ($M)                                           0
Yearly budget for international tourism promotion ($M)                             1
Governmental resources allocated to tourism promotion ($M)                         5
Public infrastructure investments related to the tourism sector ($)                0
Entrepreneurial and Innovation funds allocated to tourism related projects ($)     0
Seed funds allocated to the tourism sector ($)                                     1
Governmental funds devoted to tourism development ($)                              0
Number of regional strategy plans for tourism development                          0
dtype: int64


# Impute data for NaNs - strategy 1: replace with 0 (assumption explained above)

chile_data2['Ski resorts'] = chile_data2['Ski resorts'].fillna(0)
chile_data2['Major shopping centers'] = chile_data2['Major shopping centers'].fillna(0)
chile_data2['Number of vineyards'] = chile_data2['Number of vineyards'].fillna(0)


# Impute data for NaNs - strategy 2: replace with mean (assumption explained above)

imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')

chile_data2[['Seed funds allocated to the tourism sector ($)',
           'Governmental resources allocated to tourism promotion ($M)',
           'Yearly budget for international tourism promotion ($M)',
           'Illegal commerce',
           'Crime index']] = imputer.fit_transform(
    chile_data2[['Seed funds allocated to the tourism sector ($)',
                'Governmental resources allocated to tourism promotion ($M)',
                'Yearly budget for international tourism promotion ($M)',
                'Illegal commerce',
                'Crime index']])


# Check NaNs in the dataset to make sure

chile_data2.isnull().sum(axis = 0)

Regions                                                                           0
Density of restaurants per 100,000 habitants                                      0
People working at restaurants per 10,000 habitants                                0
Car rental agencies                                                               0
Hospital beds per 10,000 habitants                                                0
Density of ATM machines per 100,000 habitants                                     0
Spas                                                                              0
Casinos per 100,000 habitants                                                     0
Golf courses                                                                      0
Artwork Centers                                                                   0
Density of tourist guides per 100,000 habitants                                   0
Thermal centers                                                                   0
Density of sports centers per 10,000 habitants                                    0
Ski resorts                                                                       0
Major shopping centers                                                            0
Phone service penetration pero 1,000 habitants                                    0
Density of service stations                                                       0
Number of certified tourism-operators                                             0
Number of vineyards                                                               0
Percentage of crime perception                                                    0
Percentage of homes with at least one crime victim per year                       0
Density of murders per one million habitants                                      0
Density of public health complaints per one million habitants                     0
Crime index                                                                       0
Allocated budget for public safety (millions of chilean pesos)                    0
Porcentage of homes that reported at least one crime                              0
Number of reported crimes                                                         0
Number of crimes investigated and resolved                                        0
Number of accidents (roads; air; water)                                           0
Illegal commerce                                                                  0
Number of police units                                                            0
Unemployment rate                                                                 0
Poverty rate                                                                      0
Industrial and Intellectual property crimes                                       0
Yearly strikes                                                                    0
Average length of strikes                                                         0
Estimated daily cost per individual participating on a strike                     0
Banks per one million habitants                                                   0
Temporary residents                                                               0
Exports (volume in million chilean pesos)                                         0
Density of tourism offices per one million habitants                              0
Number of tourism information offices                                             0
Average number of internet searches for main tourist attractions                  0
Yearly budget for tourism promotion ($M)                                          0
Yearly budget for international tourism promotion ($M)                            0
Governmental resources allocated to tourism promotion ($M)                        0
Public infrastructure investments related to the tourism sector ($)               0
Entrepreneurial and Innovation funds allocated to tourism related projects ($)    0
Seed funds allocated to the tourism sector ($)                                    0
Governmental funds devoted to tourism development ($)                             0
Number of regional strategy plans for tourism development                         0
dtype: int64


# Since the orders of regions are the same in both dataframe, I just put the two dataframe together, without using merge

chile_data = pd.concat([chile_data1, chile_data2.drop(columns = 'Regions')], axis = 1)


chile_data.shape

(15, 132)


# Standardize data for applying PCA

# Create a copy
df = chile_data.copy()

# Standardize
scaler = StandardScaler()

df.loc[:, df.columns != 'Regions'] = scaler.fit_transform(df.loc[:, df.columns != 'Regions'])

# Set region as an index column
df = df.set_index('Regions')


# Calculate eigenvalues and vectors
cov_mat = np.cov(df.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)

# Print 
# print('Eigenvectors \n%s' %eig_vec)
# print('\nEigenvalues \n%s' %eig_val)


# Run PCA and fit the model

myPCA = PCA()
x     = myPCA.fit(df)

# Plotting the varaince explained by each component
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
pass


# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass


# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()

array([0.3321481 , 0.4649771 , 0.55703332, 0.64563531, 0.71332672,
       0.76256198, 0.80921085, 0.84886898, 0.88708633, 0.92034831,
       0.94679184, 0.96944709, 0.9877715 , 1.        , 1.        ])


pca = PCA(n_components = 6)
pca_model = pca.fit(df)

# Calculate loadings

### print("The loadings are are \n {}".format(pca_model.components_))

# See the importance of each feature for principle components for each component separately
# These are results for the Principal Component 1 (sorted)

### pd.DataFrame(np.column_stack((df.columns, pca.components_[0]))).sort_values(by = 1, ascending = False)


# Calculate factor scores
pca_model = pca.fit_transform(df)

PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'])
print("\n The Factor scores are")
PCcomponents

 The Factor scores are


# Different variables in each component

# Fit the model
myPCA = PCA(n_components = 6)
pca_model = myPCA.fit(df)
y_axis = [0,0,0,0,0,0]
for i in range(0,6):
    y_axis[i]=[np.mean(pca_model.components_[i][0:24]),   # 01 CULTURAL HERITAGE
               np.mean(pca_model.components_[i][24:47]),  # 02 NATURAL RESOURCES
               np.mean(pca_model.components_[i][47:59]),  # 03 WORKFORCE DEVELOPMENT
               np.mean(pca_model.components_[i][59:69]),  # 04 TOURISM INFRASTRUCTURE
               np.mean(pca_model.components_[i][69:81]),  # 05 TOURISM MOBILITY
               np.mean(pca_model.components_[i][81:99]),  # 06 TOURISM-RELATED SERVICES
               np.mean(pca_model.components_[i][99:111]), # 07 SECURITY AND SAFETY
               np.mean(pca_model.components_[i][111:120]),# 08 ECONOMIC PERFORMANCE
               np.mean(pca_model.components_[i][120:126]),# 09 TOURISM PROMOTION
               np.mean(pca_model.components_[i][126:131])]# 10 GOVERNMENTAL INVOLVEMENT AND EFFICIENCY

# Plot
x_axis = ['CULTURAL HERITAGE', 
          'NATURAL RESOURCES', 
          'WORKFORCE DEVELOPMENT', 
          'TOURISM INFRASTRUCTURE', 
          'TOURISM MOBILITY', 
          'TOURISM-RELATED SERVICES', 
          'SECURITY AND SAFETY ', 
          'ECONOMIC PERFORMANCE', 
          'TOURISM PROMOTION', 
          'GOVERNMENTAL INVOLVEMENT AND EFFICIENCY']

plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'black', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.xticks(rotation = 90)
plt.title('Variable contributions to each principal component')
plt.legend()
pass


# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((df.columns, 
                                        pca_model.components_[0] * pca_model.explained_variance_ratio_[0],
                                        pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
                                        pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
                                        pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
                                        pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
                                        pca_model.components_[5] * pca_model.explained_variance_ratio_[5])))
weights = weights.set_index(0)

# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)

# Print
weights.head()


# Create a dataframe for relevant variables
dim1 = df.iloc[:, 0:24].mul(weights['weighted_average'][0:24], axis = 1)

# Create a score ranking
dim1['Ranking 1'] = dim1.sum(axis = 1)

# Sort by score
dim1.sort_values(by = 'Ranking 1', ascending = False).head()


dim1.sort_values(by = 'Ranking 1',
                 ascending = True).plot.barh(y = 'Ranking 1',
                                             title = '01 CULTURAL HERITAGE AND EVENTS')
pass


# Create a dataframe for relevant variables
dim2 = df.iloc[:, 24:47].mul(weights['weighted_average'][24:47], axis = 1)

# Create a score ranking
dim2['Ranking 2'] = dim2.sum(axis = 1)

# Sort by score
dim2.sort_values(by = 'Ranking 2', ascending = False).head()


dim2.sort_values(by = 'Ranking 2',
                 ascending = True).plot.barh(y = 'Ranking 2',
                                             title = '02 NATURAL RESOURCES AND SUSTAINABILITY')
pass


# Create a dataframe for relevant variables
dim3 = df.iloc[:, 47:59].mul(weights['weighted_average'][47:59], axis = 1)

# Create a score ranking
dim3['Ranking 3'] = dim3.sum(axis = 1)

# Sort by score
dim3.sort_values(by = 'Ranking 3', ascending = False).head()


dim3.sort_values(by = 'Ranking 3',
                 ascending = True).plot.barh(y = 'Ranking 3',
                                             title = '03 HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT')
pass


# Create a dataframe for relevant variables
dim4 = df.iloc[:, 59:69].mul(weights['weighted_average'][59:69], axis = 1)

# Create a score ranking
dim4['Ranking 4'] = dim4.sum(axis = 1)

# Sort by score
dim4.sort_values(by = 'Ranking 4', ascending = False).head()


dim4.sort_values(by = 'Ranking 4',
                 ascending = True).plot.barh(y = 'Ranking 4',
                                             title = '04 TOURISM INFRASTRUCTURE')
pass


# Create a dataframe for relevant variables
dim5 = df.iloc[:, 69:81].mul(weights['weighted_average'][69:81], axis = 1)

# Create a score ranking
dim5['Ranking 5'] = dim5.sum(axis = 1)

# Sort by score
dim5.sort_values(by = 'Ranking 5', ascending = False).head()


dim5.sort_values(by = 'Ranking 5',
                 ascending = True).plot.barh(y = 'Ranking 5',
                                             title = '05 TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE')
pass


# Create a dataframe for relevant variables
dim6 = df.iloc[:, 81:99].mul(weights['weighted_average'][81:99], axis = 1)

# Create a score ranking
dim6['Ranking 6'] = dim6.sum(axis = 1)

# Sort by score
dim6.sort_values(by = 'Ranking 6', ascending = False).head()


dim6.sort_values(by = 'Ranking 6',
                 ascending = True).plot.barh(y = 'Ranking 6',
                                             title = '06 TOURISM-RELATED SERVICES')
pass


# Create a dataframe for relevant variables
dim7 = df.iloc[:, 99:111].mul(weights['weighted_average'][99:111], axis = 1)

# Create a score ranking
dim7['Ranking 7'] = dim7.sum(axis = 1)

# Sort by score
dim7.sort_values(by = 'Ranking 7', ascending = False).head()


dim7.sort_values(by = 'Ranking 7',
                 ascending = True).plot.barh(y = 'Ranking 7',
                                             title = '07 SECURITY AND SAFETY')
pass


# Create a dataframe for relevant variables
dim8 = df.iloc[:, 111:120].mul(weights['weighted_average'][111:120], axis = 1)

# Create a score ranking
dim8['Ranking 8'] = dim8.sum(axis = 1)

# Sort by score
dim8.sort_values(by = 'Ranking 8', ascending = False).head()


dim8.sort_values(by = 'Ranking 8',
                 ascending = True).plot.barh(y = 'Ranking 8',
                                             title = '08 ECONOMIC PERFORMANCE')
pass


# Create a dataframe for relevant variables
dim9 = df.iloc[:, 120:126].mul(weights['weighted_average'][120:126], axis = 1)

# Create a score ranking
dim9['Ranking 9'] = dim9.sum(axis = 1)

# Sort by score
dim9.sort_values(by = 'Ranking 9', ascending = False).head()


dim9.sort_values(by = 'Ranking 9',
                 ascending = True).plot.barh(y = 'Ranking 9',
                                             title = '09 TOURISM PROMOTION')
pass


# Create a dataframe for relevant variables
dim10 = df.iloc[:, 126:131].mul(weights['weighted_average'][126:131], axis = 1)

# Create a score ranking
dim10['Ranking 10'] = dim10.sum(axis = 1)

# Sort by score
dim10.sort_values(by = 'Ranking 10', ascending = False).head()


dim10.sort_values(by = 'Ranking 10',
                 ascending = True).plot.barh(y = 'Ranking 10',
                                             title = '10 GOVERNMENTAL INVOLVEMENT AND EFFICIENCY')
pass


scoring_data = pd.concat([dim1.iloc[:,-1:], dim2.iloc[:,-1:], dim3.iloc[:,-1:], dim4.iloc[:,-1:], dim5.iloc[:,-1:],
                          dim6.iloc[:,-1:], dim7.iloc[:,-1:], dim8.iloc[:,-1:], dim9.iloc[:,-1:], dim10.iloc[:,-1:]], 
                         axis = 1)

# Add a Overall column which is the sum across the row
scoring_data['Overall'] = scoring_data.sum(axis=1)

scoring_data


# Highlight dataframe values with colors

# attach CSS classes to each cell
scoring_data.style.highlight_null().render().split('\n')[:10]

# Create a function for negative values (red)
def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color


# Create a function for max values (yellow)
def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]


# Apply styles
scoring_data.style.applymap(color_negative_red).apply(highlight_max)


# Make a copy of the dataframe

scoring_data_c = scoring_data.copy()


# Standardize each column in the scale of 0 - 100, negative numbers are not intuitive for visual presentation

# This standardization is applied for each individual column because I am interested in the 

for i in range(0,11):
    scoring_data_c.iloc[:,i:i+1] = 100* (scoring_data_c.iloc[:,i:i+1] - scoring_data_c.iloc[:,i:i+1].min()) / (scoring_data_c.iloc[:,i:i+1].max() - scoring_data_c.iloc[:,i:i+1].min())


# In order to visualize regions as a map in Tableau
# I changed the region names to more official name and eliminate some syntax error caused by formatting issue

# Dropped the original index, which is the "Regions" column
scoring_data_c.reset_index(drop=True, inplace=True)

# Assign new "Regions" column
scoring_data_c['Regions'] = ['Arica y Parinacota',
                             'Tarapacá',
                             'Antofagasta',
                             'Atacama',
                             'Coquimbo',
                             'Valparaíso',
                             'Santiago',         # Región Metropolitana = Santiago Metropolitan Region
                             "Libertador General Bernardo O'Higgins",
                             'Maule',
                             'Bío-Bío',
                             'La Araucanía',
                             'Los Ríos',
                             'Los Lagos',
                             'Aysen del Gen. Carlos Ibáñez del Campo',
                             'Magallanes'
                            ]


# Print
scoring_data_c


# Save the final result to a .csv file

scoring_data_c.to_csv("/Users/mucongl/Documents/Study/MSCA31015_Consulting/PCA - Chile Tourism_Consulting Module 6-8/Chile_ranking_result.csv")

	Unnamed: 0	NÂ°	VARIABLE	CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR	NUMBER OF CULTURAL CENTERS	WORLD CULTURAL HERITAGE SITES	NUMBER OF ARCHEOLOGICAL SITES	NATIONAL MONUMENTS	MUSEUMS	% OF POPULATION THAT ATTENDS MUSEUMS	THEATERS	NUMBER OF THEATER PLAYS PER YEAR	LIBRARIES	GALERIES	% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP	NUMBER OF EXHIBITS	ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR	MAJOR SPORTS EVENTS PER YEAR	OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS	ARTWORK SITES	POPULAR ARCHITECTURE SITES	HISTORICAL SITES	LOCAL MARKETS	CULTURAL SITES LEVEL III (INTERNATIONAL)	CULTURA SITES LEVEL II (NATIONAL)	CULTURAL SITES LEVEL I (LOCAL)	HERITAGE ARCHITECTURAL HOUSES	% OF LAND THAT CORRESPONDS TO FORESTS	NATIONAL PROTECTED SITES (%)	% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS	TOXIC WASTE DISPOSAL (TONS/100 hab.)	NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED	ENVIRONMENTAL ISSUES PER MILLION HABITANTS	NUMBER OF BEACHES AND BEACH RESORTS	LAND AFFECTED BY WILDFIRES	NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)	NATURAL PROTECTED SITES LEVEL II (NATIONAL)	RIVERS, LAKES AND WATERFALLS	MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS	GEISERS AND THERMAL CENTERS	PIERS AND SEASHORES	GLACIERS AND WINTER VACATION LOCATIONS	VALLEYS	DESERTS AND DUNES	ISLANDS AND PENINSULAS	PALEONTOLOGY SITES	HIKING TRAILS	PRESERVED SITES	SEASHORE PROTECTED SITES	BIOSHPERE RESERVES	% AVAILABLE WORKFORCE	% POPULATION ORIENTED TOWARDS TOURISM	AVERAGE MONTHLY INCOME (CHILEAN PESOS)	5 POPULATION WITH PRIMARY EDUCATION	% POPULATION WITH SECONDARY EDUCATION	AVERAGE NUMBER OF YEARS STUDYING	HIGHER EDUCATION AND TECHNICAL INSTITUTIONS	TOURISM-ORIENTED INSTITUTIONS	NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS	AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS	DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)	CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS	% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR	ROOMS PER 1000 HABITANTS	NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.	TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)	AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR	AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND	NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION	NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS	TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS	TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)	NATIONAL TOURISTS ARRIVALS	INTERNATIONAL TOURISTS ARRIVALS	NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY	DENSITY OF AIRPORTS	DENSITY OF ROADS AND HIGHWAYS	% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)	NUMBER OF VEHICLES	VISITORS TO PROTECTED SITES	NUMBER OF CRUISES THAT ARRIVE PER YEAR	TOURIST'S ARRIVALS THROUGH BORDER LINES	SECONDARY ROADS (KMS)	NUMBER OF INTERNATIONAL BORDER GATES
0	NaN	XV	Arica y Parinacota	31.0	2	0	30	28	4	22.1	2	105	12	2	25.4	1	0	0	0	4	32	4	3	9	32	7	59	-	21.9	0.4600	58.000000	1	0.000000	5	-	13	13	4	6	6	2	0	8	2	0	0	2	-	0	1	42.556000	0.880000	$ 293,648	94.1	83.8	11.100000	53.00000	2	193	11	20.038000	356	23.69000	18.740000	6,544	33.010000	37.0	2.3	5	3	0.0	0	97,454	34,186	1,151,575	5.273000	2.129000	17.35	45,248	15,045	13	167,211	4.67	3
1	NaN	I	TarapacÃ¡	0.0	5	1	13	73	5	20.8	2	178	12	1	12.6	1	0	0	0	0	34	10	1	6	34	12	0	0.2	9.1	0.0300	76.030000	1	0.000000	16	-	2	6	1	1	7	5	0	5	6	4	2	3	5	0	0	68.563000	1.450000	$ 381,466	91.7	66.7	10.700000	42.00000	5	255	19	22.180000	380	23.03000	22.170000	11,108	41.430000	42.8	2.1	5	10	0.0	11	235,365	40,919	19,560	4.185000	4.021000	15.90	81,182	17,161	0	434,727	184.10	1
2	NaN	II	Antofagasta	1.0	9	0	28	81	16	27.4	8	203	15	2	5.7	2	1	0	3	1	24	37	0	4	31	26	63	-	2.8	0.0300	39.560000	2	0.000000	22	-	5	28	8	13	3	14	1	6	10	6	4	0	5	0	0	54.486000	1.080000	$ 475,866	91.7	66.7	10.600000	40.00000	7	529	47	20.446000	184	24.55000	22.760000	19,920	35.020000	44.6	1.8	2	10	17.0	15	413,922	84,195	22,898	2.024400	3.332000	52.25	112,607	315,888	0	115,100	23.00	5
3	NaN	III	Atacama	8.0	10	0	8	35	7	20.0	0	144	12	0	7.5	0	0	0	0	0	18	5	0	2	29	7	33	-	2.0	0.0200	40.760000	0	3.930000	17	NaN	7	23	2	7	2	10	0	3	13	1	3	2	5	1	0	51.844000	0.940000	$ 379,971	89.1	73.2	10.300000	47.00000	1	108	16	18.479000	578	19.43000	18.490000	9,674	29.250000	36.0	1.9	1	3	0.0	15	168,508	14,222	2,416	0.000000	3.635000	24.39	55,812	24,873	0	2,552	11.33	2
4	NaN	IV	Coquimbo	23.0	7	0	2	52	7	18.3	4	80	22	2	1.7	0	1	0	7	1	69	2	1	0	29	27	97	0.8	0.4	0.3500	25.450000	1	0.000000	37	1.50	4	16	7	0	1	21	0	18	0	9	1	0	5	2	1	56.498000	0.920000	$ 338,014	92.7	71.2	9.700000	43.00000	7	870	85	6.963000	1074	15.50000	15.630000	24,346	12.000000	35.2	2.8	4	9	383.9	750	205,850	25,803	2,786	0.000000	1.912000	283.37	116,263	57,234	0	15,265	228.58	1
5	NaN	V	ValparaÃso	14.0	37	2	24	161	12	25.7	7	322	56	7	3.2	3	3	1	4	2	48	21	4	46	90	36	720	6.5	2.7	2.1100	38.460000	13	0.000000	59	6.64	13	71	3	0	3	14	3	22	1	0	0	14	5	3	2	53.534000	0.990000	$ 311,264	92.9	73.5	10.600000	27.00000	21	3,949	316	12.209000	3661	17.21000	13.390000	44,504	21.180000	29.7	2.0	14	27	920.0	257	430,436	106,915	317,309	0.649400	0.197000	280.95	316,618	146,161	31	436,195	93.86	1
6	NaN	RM	Metropolitana	4.0	56	0	1	404	35	22.5	70	6558	127	25	4.1	30	0	0	3	2	5	9	4	4	85	112	274	6.9	0.9	7.2500	47.030000	16	0.000000	0	0.04	3	3	0	3	4	0	5	9	0	0	0	0	5	0	0	55.901000	6.060000	$ 421,484	93.1	70.5	11.200000	19.00000	30	12,881	1261	4.834000	15221	38.13000	4.330000	43,634	9.320000	59.8	2.2	15	133	754.2	236	448,887	765,681	1,091,111	1.281000	0.731000	541.32	1,306,140	83,459	0	1,147,039	248.50	1
7	NaN	VI	O'Higgins	32.0	13	1	9	67	6	21.9	13	251	36	0	1.9	1	7	2	3	16	29	3	1	0	28	15	229	11.3	2.8	0.9200	27.150000	4	1.280000	12	2.47	5	22	1	3	2	2	7	5	2	1	1	7	5	0	0	54.662000	0.620000	$ 308,068	95.5	65.0	9.500000	23.00000	7	352	15	9.992000	2833	17.46000	8.010000	14,526	6.340000	15.4	2.2	2	7	10.0	0	73,478	7,066	-	0.000000	1.352000	134.50	164,204	11,073	0	0	62.12	0
8	NaN	VII	Maule	29.0	17	0	1	54	7	11.1	9	657	29	1	1.6	1	7	0	0	10	27	4	3	0	37	28	19	12.7	0.6	0.4100	32.860000	5	0.000000	18	0.82	0	20	23	12	7	5	6	13	3	0	0	9	5	0	0	48.164000	0.240000	$ 244,231	93.4	73.1	9.000000	24.00000	4	364	37	8.700000	2898	14.49000	6.100000	12,278	8.670000	28.4	2.0	3	14	0.0	0	167,293	8,935	3,942	0.000000	1.480000	197.65	185,728	64,500	0	853	232.90	2
9	NaN	VIII	BiobÃo	5.0	32	0	0	59	20	18.9	12	488	63	7	3.9	10	0	0	0	4	13	23	2	0	10	54	135	20.7	2.8	0.9600	31.680000	2	1.070000	25	0.73	3	7	19	3	2	4	3	2	0	1	8	0	5	0	0	48.651000	1.230000	$ 290,367	92.7	71.1	9.900000	23.00000	17	4,023	320	1.612000	2334	16.04000	4.930000	20,802	7.890000	36.0	1.9	5	12	25.0	9	393,481	31,409	1,546	0.000000	1.021000	400.89	312,085	49,841	0	828	642.26	1
10	NaN	IX	AraucanÃa	18.0	13	0	2	96	5	13.2	8	179	58	4	30.1	0	5	3	0	13	16	6	10	0	12	31	20	29.4	9.6	0.4300	36.790000	6	0.000000	25	0.16	22	79	51	10	17	3	7	10	0	1	3	23	5	0	1	52.282000	2.300000	$ 251,081	94.5	72.0	9.100000	29.00000	8	709	67	9.085000	709	11.21000	9.900000	26,140	16.090000	37.4	2.5	15	7	24.0	25	200,377	38,524	130,713	0.000000	1.615000	236.97	124,956	228,921	0	112,246	469.74	3
11	NaN	XIV	Los RÃos	3.0	9	0	1	33	8	16.2	3	94	12	2	16.7	2	1	0	1	0	10	5	2	0	10	19	55	46.1	6.9	0.3100	35.410000	0	0.000000	10	0.14	2	31	13	3	14	4	0	1	0	5	1	0	-	0	1	47.218000	0.410000	$ 268,648	93.6	64.8	9.300000	31.00000	4	444	58	8.698000	868	17.28000	18.910000	12,846	11.810000	37.9	1.7	9	1	54.1	0	113,900	16,070	11,145	0.000000	2.019000	133.02	51,811	376	7	0	42.67	1
12	NaN	X	Los Lagos	1.0	24	1	0	64	11	19.8	8	272	31	1	20.8	2	0	0	0	0	38	3	3	0	57	30	94	56.3	15.9	0.1800	34.770000	1	0.000000	18	0.00	12	41	17	10	9	10	3	6	0	31	0	0	7	3	1	54.555000	14.970000	$ 291,431	92.9	66.7	9.100000	31.00000	10	856	72	20.928000	2672	14.08000	23.970000	35,548	23.820000	31.6	1.8	27	13	54.1	48	275,043	105,048	259,411	10.929900	1.893000	135.48	129,882	486,725	28	192,977	660.16	3
13	NaN	XI	AysÃ©n	0.0	4	0	2	17	0	14.8	0	49	12	0	21.8	0	0	0	0	0	24	2	0	1	20	10	0	44.4	39.4	0.0200	43.890000	0	0.000000	0	0.01	21	93	25	8	6	5	23	3	0	8	3	6	6	0	2	61.016000	1.050000	$ 418,044	95.3	68.5	9.500000	66.00000	3	64	6	67.765000	83	6.83000	37.600000	8,020	23.060000	21.7	1.7	0	1	0.0	19	28,584	10,763	90,876	0.000000	4.089000	3.70	19,348	32,030	0	56,201	5.00	4
14	NaN	XII	Magallanes y AntÃ¡rtica	0.0	3	0	2	45	6	27.7	2	4	14	0	22.7	3	0	0	0	2	16	16	0	2	24	11	34	20.2	57.3	0.0400	40.580000	3	0.000000	0	0.01	19	62	23	10	0	17	20	10	0	0	1	1	5	1	1	51.872000	0.060000	$ 572,203	93.6	77.6	10.200000	60.00000	6	380	55	137.244000	222	12.94000	41.530000	12,436	67.760000	37.1	2.0	5	3	0.0	12	84,034	109,092	262,190	0.165000	3.632000	6.02	46,336	283,629	18	388,598	0.00	5
15	NaN	TOTAL	NaN	NaN	241	5	123	1269	149	300.4	148	9584	511	54	NaN	56	25	6	21	55	403	150	34	74	528	425	1832	255.4712331	175.1	13.5195	608.394109	55	6.287195	264	12.503807	131	515	197	89	6	116	78	121	37	67	27	67	68	10	10	801.801249	33.203999	5245786.493	NaN	NaN	149.927455	557.54502	132	25977	2385	369.173552	34073	271.87922	266.449646	302326	346.657737	530.6	30.9	112	253	2242.3	1397	3336612	1398828	3367478	24.507707	33.057941	2463.76	3068220	1816916	97	3069792	2909.14	33

	Regions	CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR	NUMBER OF CULTURAL CENTERS	WORLD CULTURAL HERITAGE SITES	NUMBER OF ARCHEOLOGICAL SITES	NATIONAL MONUMENTS	MUSEUMS	% OF POPULATION THAT ATTENDS MUSEUMS	THEATERS	NUMBER OF THEATER PLAYS PER YEAR	LIBRARIES	GALERIES	% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP	NUMBER OF EXHIBITS	ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR	MAJOR SPORTS EVENTS PER YEAR	OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS	ARTWORK SITES	POPULAR ARCHITECTURE SITES	HISTORICAL SITES	LOCAL MARKETS	CULTURAL SITES LEVEL III (INTERNATIONAL)	CULTURA SITES LEVEL II (NATIONAL)	CULTURAL SITES LEVEL I (LOCAL)	HERITAGE ARCHITECTURAL HOUSES	% OF LAND THAT CORRESPONDS TO FORESTS	NATIONAL PROTECTED SITES (%)	% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS	TOXIC WASTE DISPOSAL (TONS/100 hab.)	NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED	ENVIRONMENTAL ISSUES PER MILLION HABITANTS	NUMBER OF BEACHES AND BEACH RESORTS	LAND AFFECTED BY WILDFIRES	NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)	NATURAL PROTECTED SITES LEVEL II (NATIONAL)	RIVERS, LAKES AND WATERFALLS	MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS	GEISERS AND THERMAL CENTERS	PIERS AND SEASHORES	GLACIERS AND WINTER VACATION LOCATIONS	VALLEYS	DESERTS AND DUNES	ISLANDS AND PENINSULAS	PALEONTOLOGY SITES	HIKING TRAILS	PRESERVED SITES	SEASHORE PROTECTED SITES	BIOSHPERE RESERVES	% AVAILABLE WORKFORCE	% POPULATION ORIENTED TOWARDS TOURISM	AVERAGE MONTHLY INCOME (CHILEAN PESOS)	5 POPULATION WITH PRIMARY EDUCATION	% POPULATION WITH SECONDARY EDUCATION	AVERAGE NUMBER OF YEARS STUDYING	HIGHER EDUCATION AND TECHNICAL INSTITUTIONS	TOURISM-ORIENTED INSTITUTIONS	NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS	AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS	DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)	CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS	% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR	ROOMS PER 1000 HABITANTS	NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.	TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)	AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR	AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND	NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION	NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS	TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS	TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)	NATIONAL TOURISTS ARRIVALS	INTERNATIONAL TOURISTS ARRIVALS	NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY	DENSITY OF AIRPORTS	DENSITY OF ROADS AND HIGHWAYS	% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)	NUMBER OF VEHICLES	VISITORS TO PROTECTED SITES	NUMBER OF CRUISES THAT ARRIVE PER YEAR	TOURIST'S ARRIVALS THROUGH BORDER LINES	SECONDARY ROADS (KMS)	NUMBER OF INTERNATIONAL BORDER GATES
0	Arica y Parinacota	31.0	2	0	30	28	4	22.1	2	105	12	2	25.4	1	0	0	0	4	32	4	3	9	32	7	59	-	21.9	0.46	58.00	1	0.00	5	-	13	13	4	6	6	2	0	8	2	0	0	2	-	0	1	42.556	0.88	$ 293,648	94.1	83.8	11.1	53.0	2	193	11	20.038	356	23.69	18.74	6,544	33.01	37.0	2.3	5	3	0.0	0	97,454	34,186	1,151,575	5.2730	2.129	17.35	45,248	15,045	13	167,211	4.67	3
1	TarapacÃ¡	0.0	5	1	13	73	5	20.8	2	178	12	1	12.6	1	0	0	0	0	34	10	1	6	34	12	0	0.2	9.1	0.03	76.03	1	0.00	16	-	2	6	1	1	7	5	0	5	6	4	2	3	5	0	0	68.563	1.45	$ 381,466	91.7	66.7	10.7	42.0	5	255	19	22.180	380	23.03	22.17	11,108	41.43	42.8	2.1	5	10	0.0	11	235,365	40,919	19,560	4.1850	4.021	15.90	81,182	17,161	0	434,727	184.10	1
2	Antofagasta	1.0	9	0	28	81	16	27.4	8	203	15	2	5.7	2	1	0	3	1	24	37	0	4	31	26	63	-	2.8	0.03	39.56	2	0.00	22	-	5	28	8	13	3	14	1	6	10	6	4	0	5	0	0	54.486	1.08	$ 475,866	91.7	66.7	10.6	40.0	7	529	47	20.446	184	24.55	22.76	19,920	35.02	44.6	1.8	2	10	17.0	15	413,922	84,195	22,898	2.0244	3.332	52.25	112,607	315,888	0	115,100	23.00	5
3	Atacama	8.0	10	0	8	35	7	20.0	0	144	12	0	7.5	0	0	0	0	0	18	5	0	2	29	7	33	-	2.0	0.02	40.76	0	3.93	17	NaN	7	23	2	7	2	10	0	3	13	1	3	2	5	1	0	51.844	0.94	$ 379,971	89.1	73.2	10.3	47.0	1	108	16	18.479	578	19.43	18.49	9,674	29.25	36.0	1.9	1	3	0.0	15	168,508	14,222	2,416	0.0000	3.635	24.39	55,812	24,873	0	2,552	11.33	2
4	Coquimbo	23.0	7	0	2	52	7	18.3	4	80	22	2	1.7	0	1	0	7	1	69	2	1	0	29	27	97	0.8	0.4	0.35	25.45	1	0.00	37	1.50	4	16	7	0	1	21	0	18	0	9	1	0	5	2	1	56.498	0.92	$ 338,014	92.7	71.2	9.7	43.0	7	870	85	6.963	1074	15.50	15.63	24,346	12.00	35.2	2.8	4	9	383.9	750	205,850	25,803	2,786	0.0000	1.912	283.37	116,263	57,234	0	15,265	228.58	1
5	ValparaÃso	14.0	37	2	24	161	12	25.7	7	322	56	7	3.2	3	3	1	4	2	48	21	4	46	90	36	720	6.5	2.7	2.11	38.46	13	0.00	59	6.64	13	71	3	0	3	14	3	22	1	0	0	14	5	3	2	53.534	0.99	$ 311,264	92.9	73.5	10.6	27.0	21	3,949	316	12.209	3661	17.21	13.39	44,504	21.18	29.7	2.0	14	27	920.0	257	430,436	106,915	317,309	0.6494	0.197	280.95	316,618	146,161	31	436,195	93.86	1
6	Metropolitana	4.0	56	0	1	404	35	22.5	70	6558	127	25	4.1	30	0	0	3	2	5	9	4	4	85	112	274	6.9	0.9	7.25	47.03	16	0.00	0	0.04	3	3	0	3	4	0	5	9	0	0	0	0	5	0	0	55.901	6.06	$ 421,484	93.1	70.5	11.2	19.0	30	12,881	1261	4.834	15221	38.13	4.33	43,634	9.32	59.8	2.2	15	133	754.2	236	448,887	765,681	1,091,111	1.2810	0.731	541.32	1,306,140	83,459	0	1,147,039	248.50	1
7	O'Higgins	32.0	13	1	9	67	6	21.9	13	251	36	0	1.9	1	7	2	3	16	29	3	1	0	28	15	229	11.3	2.8	0.92	27.15	4	1.28	12	2.47	5	22	1	3	2	2	7	5	2	1	1	7	5	0	0	54.662	0.62	$ 308,068	95.5	65.0	9.5	23.0	7	352	15	9.992	2833	17.46	8.01	14,526	6.34	15.4	2.2	2	7	10.0	0	73,478	7,066	-	0.0000	1.352	134.50	164,204	11,073	0	0	62.12	0
8	Maule	29.0	17	0	1	54	7	11.1	9	657	29	1	1.6	1	7	0	0	10	27	4	3	0	37	28	19	12.7	0.6	0.41	32.86	5	0.00	18	0.82	0	20	23	12	7	5	6	13	3	0	0	9	5	0	0	48.164	0.24	$ 244,231	93.4	73.1	9.0	24.0	4	364	37	8.700	2898	14.49	6.10	12,278	8.67	28.4	2.0	3	14	0.0	0	167,293	8,935	3,942	0.0000	1.480	197.65	185,728	64,500	0	853	232.90	2
9	BiobÃo	5.0	32	0	0	59	20	18.9	12	488	63	7	3.9	10	0	0	0	4	13	23	2	0	10	54	135	20.7	2.8	0.96	31.68	2	1.07	25	0.73	3	7	19	3	2	4	3	2	0	1	8	0	5	0	0	48.651	1.23	$ 290,367	92.7	71.1	9.9	23.0	17	4,023	320	1.612	2334	16.04	4.93	20,802	7.89	36.0	1.9	5	12	25.0	9	393,481	31,409	1,546	0.0000	1.021	400.89	312,085	49,841	0	828	642.26	1
10	AraucanÃa	18.0	13	0	2	96	5	13.2	8	179	58	4	30.1	0	5	3	0	13	16	6	10	0	12	31	20	29.4	9.6	0.43	36.79	6	0.00	25	0.16	22	79	51	10	17	3	7	10	0	1	3	23	5	0	1	52.282	2.30	$ 251,081	94.5	72.0	9.1	29.0	8	709	67	9.085	709	11.21	9.90	26,140	16.09	37.4	2.5	15	7	24.0	25	200,377	38,524	130,713	0.0000	1.615	236.97	124,956	228,921	0	112,246	469.74	3
11	Los RÃos	3.0	9	0	1	33	8	16.2	3	94	12	2	16.7	2	1	0	1	0	10	5	2	0	10	19	55	46.1	6.9	0.31	35.41	0	0.00	10	0.14	2	31	13	3	14	4	0	1	0	5	1	0	-	0	1	47.218	0.41	$ 268,648	93.6	64.8	9.3	31.0	4	444	58	8.698	868	17.28	18.91	12,846	11.81	37.9	1.7	9	1	54.1	0	113,900	16,070	11,145	0.0000	2.019	133.02	51,811	376	7	0	42.67	1
12	Los Lagos	1.0	24	1	0	64	11	19.8	8	272	31	1	20.8	2	0	0	0	0	38	3	3	0	57	30	94	56.3	15.9	0.18	34.77	1	0.00	18	0.00	12	41	17	10	9	10	3	6	0	31	0	0	7	3	1	54.555	14.97	$ 291,431	92.9	66.7	9.1	31.0	10	856	72	20.928	2672	14.08	23.97	35,548	23.82	31.6	1.8	27	13	54.1	48	275,043	105,048	259,411	10.9299	1.893	135.48	129,882	486,725	28	192,977	660.16	3
13	AysÃ©n	0.0	4	0	2	17	0	14.8	0	49	12	0	21.8	0	0	0	0	0	24	2	0	1	20	10	0	44.4	39.4	0.02	43.89	0	0.00	0	0.01	21	93	25	8	6	5	23	3	0	8	3	6	6	0	2	61.016	1.05	$ 418,044	95.3	68.5	9.5	66.0	3	64	6	67.765	83	6.83	37.60	8,020	23.06	21.7	1.7	0	1	0.0	19	28,584	10,763	90,876	0.0000	4.089	3.70	19,348	32,030	0	56,201	5.00	4
14	Magallanes y AntÃ¡rtica	0.0	3	0	2	45	6	27.7	2	4	14	0	22.7	3	0	0	0	2	16	16	0	2	24	11	34	20.2	57.3	0.04	40.58	3	0.00	0	0.01	19	62	23	10	0	17	20	10	0	0	1	1	5	1	1	51.872	0.06	$ 572,203	93.6	77.6	10.2	60.0	6	380	55	137.244	222	12.94	41.53	12,436	67.76	37.1	2.0	5	3	0.0	12	84,034	109,092	262,190	0.1650	3.632	6.02	46,336	283,629	18	388,598	0.00	5

	Regions	Density of restaurants per 100,000 habitants	People working at restaurants per 10,000 habitants	Car rental agencies	Hospital beds per 10,000 habitants	Density of ATM machines per 100,000 habitants	Spas	Casinos per 100,000 habitants	Golf courses	Artwork Centers	Density of tourist guides per 100,000 habitants	Thermal centers	Density of sports centers per 10,000 habitants	Ski resorts	Major shopping centers	Phone service penetration pero 1,000 habitants	Density of service stations	Number of certified tourism-operators	Number of vineyards	Percentage of crime perception	Percentage of homes with at least one crime victim per year	Density of murders per one million habitants	Density of public health complaints per one million habitants	Crime index	Allocated budget for public safety (millions of chilean pesos)	Porcentage of homes that reported at least one crime	Number of reported crimes	Number of crimes investigated and resolved	Number of accidents (roads; air; water)	Illegal commerce	Number of police units	Unemployment rate	Poverty rate	Industrial and Intellectual property crimes	Yearly strikes	Average length of strikes	Estimated daily cost per individual participating on a strike	Banks per one million habitants	Temporary residents	Exports (volume in million chilean pesos)	Density of tourism offices per one million habitants	Number of tourism information offices	Average number of internet searches for main tourist attractions	Yearly budget for tourism promotion ($M)	Yearly budget for international tourism promotion ($M)	Governmental resources allocated to tourism promotion ($M)	Public infrastructure investments related to the tourism sector ($)	Entrepreneurial and Innovation funds allocated to tourism related projects ($)	Seed funds allocated to the tourism sector ($)	Governmental funds devoted to tourism development ($)	Number of regional strategy plans for tourism development	Unnamed: 51
0	Arica y Parinacota (XV)	35.857	19.563	12	21.619	43.766	0	5.273	1	20	20.038	5	0.000	-	-	240.55	17	6	-	0.318	0.260	42.184	15.819	0.513	11,884,613	0.426	26,467	2,387	981	319	265	7.3	8.2	84.369	1	11	253	94.915	45,560	137	5.273	6,517	1,060,490	193,364	118,413	92,641	36,370,835	11,757,543	446,922	24,729,573	1	NaN
1	TarapacÃ¡ (I)	25.947	26.616	2	18.958	60.264	0	4.185	3	9	22.180	5	40.134	-	2	213.07	28	0	-	0.421	0.468	87.884	0.000	0.516	14,740,693	0.398	76,896	1,862	1,105	593	592	4.8	13.4	485.457	6	14.9	59,745	138.104	83,265	3,236	4.185	7,600	1,750,481	74,404	13,600	-	42,364,100	12,796,789	355,208	29,714,382	2	NaN
2	Antofagasta (II)	37.248	35.446	22	29.960	69.233	3	4.049	3	36	20.446	2	14.292	-	3	259.23	39	2	-	0.546	0.314	109.315	0.000	0.381	22,383,612	0.495	123,017	4,228	2,324	7	765	6.2	7.3	12.146	13	9.6	30,220	178.143	238,010	16,666	4.049	16,280	2,846,340	200,000	149,000	149,744	38,262,875	29,066,423	192,572	50,841,622	1	NaN
3	Atacama (III)	44.823	26.933	19	25.478	56.225	1	3.932	1	9	18.479	3	32.437	-	-	184.01	35	1	-	0.390	0.197	66.841	0.000	0.488	17,068,077	0.533	21,705	3,125	965	14	504	6.4	10.2	15.727	3	29.3	17,860	145.477	53,046	3,456	3.932	11,614	556,668	94,100	187,035	-	35,948,639	16,986,593	233,225	30,224,699	2	NaN
4	Coquimbo (IV)	37.632	25.165	9	17.805	47.745	3	3.316	3	13	6.963	3	14.091	-	1	150.06	72	3	-	0.288	0.218	155.833	0.000	0.396	20,225,710	0.449	69,974	4,203	1,831	100	1,082	6.0	12.3	11.605	3	29.0	1,431	112.730	275,447	2,229	1.658	11,056	5,364,222	189,900	72,917	-	64,917,630	33,222,779	673,800	44,790,979	3	NaN
5	ValparaÃso (V)	52.408	47.050	16	27.756	55.850	9	1.948	12	44	12.209	3	18.710	2	8	230.65	179	3	5	0.311	0.291	87.671	1.948	0.432	24,137,492	0.445	236,177	19,176	6,765	166	2,761	7.5	11.6	50.654	10	10.7	16,641	143.520	1,557,887	4,287	1.299	5,649	3,058,423	129,481	63,996	1,079,295	101,802,434	24,239,428	811,772	51,647,041	1	NaN
6	Metropolitana (RM)	9.223	16.914	47	25.508	65.499	0	0.000	21	42	4.834	4	14.717	5	33	331.61	507	39	1	0.372	0.295	65.334	0.660	0.47	60,400,444	0.424	1,146,510	53,517	23,242	7,605	9,385	7.7	8.8	39.596	102	12.3	147,198	1295.113	7,307,884	6,196	0.330	19,352	13,709,951	56,982	-	-	230,562,951	43,552,270	62,052	90,875,903	1	NaN
7	O'Higgins (VI)	18.959	49.422	6	19.946	43.427	0	2.562	3	39	9.992	2	14.950	1	2	117.30	114	3	7	0.203	0.220	51.241	0.000	0.396	18,182,163	0.477	111,020	4,894	2,451	762	1,136	6.0	9.9	11.529	7	10.2	26,831	95.805	265,601	3,056	1.281	4,700	754,000	179,000	6,000	345,000	50,200,910	17,850,727	444,491	43,045,390	1	NaN
8	Maule (VII)	15.637	19.943	10	23.852	35.899	9	1.101	1	24	8.700	5	25.383	-	3	109.73	130	0	2	0.245	0.227	27.530	1.101	0.427	25,936,948	0.530	124,820	6,018	4,771	194	1,875	6.4	15.8	11.012	5	10.7	1,371	48.346	165,417	1,311	1.101	7,183	647,510	168,850	8,000	218,000	97,730,159	19,292,650	486,500	48,826,193	2	NaN
9	BiobÃo (VIII)	14.128	14.004	16	27.160	38.194	2	1.612	6	26	1.612	3	20.531	1	3	162.84	211	1	-	0.360	0.310	63.925	0.000	0.42	41,208,340	0.410	292,281	19,222	5,713	37	3,193	7.9	15.8	12.892	12	11.8	15,518	188.607	305,920	4,475	1.074	7,362	3,548,528	317,000	44,000	319,660	137,998,425	28,228,357	1,646,127	88,172,439	3	NaN
10	AraucanÃa (IX)	15.986	15.687	25	25.887	40.826	23	2.300	2	16	9.085	13	22.736	4	1	135.88	118	5	-	0.381	0.288	78.203	1.150	0.369	29,000,528	0.469	140,063	8,685	3,161	323	1,550	8.0	18.1	20.701	4	30.3	5,856	232.887	395,583	372	1.150	9,488	1,842,375	108,799	27,507	-	67,212,297	31,050,589	-	54,529,735	2	NaN
11	Los RÃos (XIV)	34.512	26.207	9	30.472	42.649	2	2.806	1	40	8.698	12	0.000	1	-	191.32	44	1	-	0.274	0.270	101.011	0.000	0.298	15,834,434	0.440	22,076	3,235	818	29	773	6.4	14.3	11.223	4	11.3	6,171	51.623	71,200	71	2.806	7,109	1,476,220	253,292	35,000	13,000	49,376,288	22,065,484	486,875	32,409,855	1	NaN
12	Los Lagos (X)	36.415	27.360	24	22.644	47.298	2	4.186	2	27	20.928	8	25.728	2	2	162.58	86	6	-	0.323	0.201	104.641	0.000	0.374	19,901,362	0.426	85,949	8,729	2,023	192	1,409	3.5	11.8	23.719	2	8	2,160	994.622	122,157	2,251	4.186	4,708	7,108,761	264,511	96,915	63,500	101,170,739	40,816,121	737,875	62,581,190	2	NaN
13	AysÃ©n (XI)	76.509	40.987	15	27.543	43.720	0	10.930	0	57	67.765	4	24.264	1	-	170.81	20	0	-	0.351	0.256	174.879	10.930	-	15,972,532	0.445	14,119	3,349	422	-	469	4.4	5.2	32.790	1	7	1,953	106.083	13,667	379	10.930	5,660	200,654	65,000	454,500	536,488	32,558,406	13,692,259	794,614	29,462,726	2	NaN
14	Magallanes y AntÃ¡rtica (XII)	59.008	59.340	12	34.676	71.606	0	13.260	1	15	137.244	0	18.366	1	2	297.25	21	3	-	0.283	0.180	53.041	6.630	0.367	20,075,842	0.414	8,659	2,527	660	-	216	5.4	6.1	86.192	1	22	616	5.115	322,800	397	13.260	4,217	558,678	266,000	525,000	32,100	30,955,753	18,036,736	357,752	30,288,105	1	NaN
15	AÃO	2010	2006	2011	2011	2010	2011	2011	2011	2011	2011	2010	2004	2011	2011	2011	2012	2012	NaN	2010	2010	2010	2010	2010	2010	2010	2008	2010	2008	2011	2011	2011	2009	2010	2010	2010	2010	2011	2011	2009	2011	2011	2011	2011-2014	2011-2014	2011	2011	2011	Promedio 2011-2010	2011	Desde 2000 hasta 2030	NaN
16	FUENTE	INE - Informe anual de turismo	SERNATUR - Turismo y empleo en Chile	SERNATUR - Unidad de servicios turÃsticos	INE -âEstadÃsticas de saludâ â âDotac...	SBIF -"InformaciÃ³n financieraâ â CategorÃ...	Atractivos TurÃsticos Base SIG SERNATUR	Superintendencia de Casinos de Juego â Estad...	FederaciÃ³n chilena de golf â Listado de clubes	SERNATUR - Unidad de servicios turÃsticos	SERNATUR - Unidad de servicios turÃsticos	SERNATUR- Micrositio de rutas termales	INE-Primer Catastro Nacional de Instalaciones ...	SERNATUR - Sitio web Chile es Tuyo	Camara Chilena de Centros Comerciales - CatÃ¡s...	SubsecretarÃa de Telecomunicaciones - Series ...	ComisiÃ³n Nacional de EnergÃa - Sistema de In...	SERNATUR - Servicio de Angencias de Viajes y T...	SIG Sernatur/ CategorÃa Realizaciones TÃ©ctic...	SubsecretarÃa de prevenciÃ³n del delito â e...	INE - Encuesta de seguridad ciudadana	INE â Anuario de Investigaciones	INE â Anuario de Investigaciones	FundaciÃ³n Paz Ciudadana - Conceptos NÂº 22: L...	DIPRES â instituciones â Ministerio del in...	INE - Encuesta de seguridad ciudadana	SubsecretarÃa de prevenciÃ³n del delito	INE â Anuario de Investigaciones	INE - Carabineros Informe Anual 2008	Carabineros de Chile - Carabineros en Cifras 2011	Carabineros de Chile - Cuenta PÃºblica	INE â Nueva encuesta nacional de empleo	Ministerio de Desarrollo Social	INE â Anuario de Investigaciones	DirecciÃ³n del Trabajo â Compendio de series...	DirecciÃ³n del Trabajo â Compendio de series...	DirecciÃ³n del Trabajo â Compendio de series...	Superintendencia de Bancos e Instituciones Fin...	Carabineros de Chile - Cuenta PÃºblica	DirecciÃ³n General de Direcciones EconÃ³micas ...	Oficina de InformaciÃ³n TurÃstica en Santiago	Oficina de InformaciÃ³n TurÃstica en Santiago	Google AdWords	Plan para el Desarrollo TurÃstico RegiÃ³n Met...	Plan para el Desarrollo TurÃstico RegiÃ³n Met...	DIPRES - Informe EjecuciÃ³n Programa Cuarto Tr...	MOP - Informe de EjecuciÃ³n Presupuestaria	DIPRES - Informe EjecuciÃ³n Programa Cuarto Tr...	DIPRES - Informe EjecuciÃ³n Programa Cuarto Tr...	SUBDERE - Informe de EjecuciÃ³n Presupuestaria...	SUBDERE - Estrategias Regionales de Desarrollo...	NaN
17	LINK	http://www.ine.cl/canales/menu/publicaciones/c...	http://www.sernatur.cl/estudios-y-estadisticas...	----------------	http://www.ine.cl/canales/menu/publicaciones/c...	http://www.sbif.cl/sbifweb/servlet/InfoFinanci...	----------------	http://www.scj.cl/estadisticas/analisis_estadi...	http://www.chilegolf.cl/es/clubes	----------------	----------------	http://www.sernatur.cl/images/lang1/nacional/m...	http://www.ine.cl/canales/sala_prensa/archivo_...	http://www.chileestuyo.cl/historias-y-novedade...	http://www.camaracentroscomerciales.cl/catastr...	http://www.subtel.gob.cl/prontus_subtel/site/a...	http://www.bencinaenlinea.cl/web2/buscador.php...	www.calidadturistica.cl	----------------	http://www.seguridadpublica.gov.cl/enusc_2010....	http://www.ine.cl/canales/chile_estadistico/en...	http://www.ine.cl/canales/chile_estadistico/es...	http://www.ine.cl/canales/chile_estadistico/es...	http://www.pazciudadana.cl/publs.php?&view=A	http://www.dipres.gob.cl/574/multipropertyvalu...	http://www.ine.cl/canales/chile_estadistico/en...	http://www.seguridadpublica.gov.cl/publicacion...	http://www.ine.cl/canales/chile_estadistico/es...	----------------	----------------	http://centroestudios.carabineros.cl/#	http://www.ine.cl/canales/chile_estadistico/me...	http://www.ministeriodesarrollosocial.gob.cl/c...	http://www.ine.cl/canales/chile_estadistico/es...	http://www.dt.gob.cl/documentacion/1612/w3-art...	http://www.dt.gob.cl/documentacion/1612/w3-art...	http://www.dt.gob.cl/documentacion/1612/w3-art...	http://www.sbif.cl/sbifweb/servlet/InfoFinanci...	http://centroestudios.carabineros.cl/#	http://www.prochile.cl/regiones_pro/archivos/r...	----------------	----------------	https://adwords.google.com	http://www.sernatur.cl/planes-regionales-de-de...	http://www.sernatur.cl/planes-regionales-de-de...	http://www.dipres.gob.cl/574/propertyvalue-142...	http://www.dirplan.cl/InformaciondePresupuesto...	http://www.dipres.gob.cl/574/propertyvalue-142...	http://www.dipres.gob.cl/574/propertyvalue-142...	http://www.subdere.cl/documentacion/fndr-ejecu...	http://www.subdere.gov.cl/documentacion/estrat...	NaN
18	UbicaciÃ³n Archivo base en carpeta interna	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\D6 SERVICIOS COMPLEMENTARIOS AL TURISM...	Dropbox\ICT Desarrollo\Variables Propuestas\D6...	Dropbox\ICT Desarrollo\Variables Propuestas\D6...	Dropbox\ICT Desarrollo\Variables Propuestas\D6...	Dropbox\ICT Desarrollo\Variables Propuestas\D6...	Dropbox\ICT Desarrollo\Variables Propuestas\D6...	ICT Desarrollo\SIG Sernatur\Atractivos TurÃst...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\ICT Desarrollo\D7 SEGURIDAD PÃBLICA Y...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\ICT Desarrollo\D7 SEGURIDAD PÃBLICA Y...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\D7 SEGURIDAD PÃBLICA Y PROTECCIÃN AL...	Dropbox\ICT Desarrollo\Variables Propuestas\D7...	Dropbox\ICT Desarrollo\Variables Propuestas\D7...	Dropbox\ICT Desarrollo\Variables Propuestas\D7...	Dropbox\D8 RENTABILIDAD Y ASPECTOS ECONÃMICOS...	Dropbox\D8 RENTABILIDAD Y ASPECTOS ECONÃMICOS...	\Dropbox\ICT Desarrollo\D8 RENTABILIDAD Y ASPE...	Dropbox\D8 RENTABILIDAD Y ASPECTOS ECONÃMICOS...	Dropbox\D8 RENTABILIDAD Y ASPECTOS ECONÃMICOS...	Dropbox\D8 RENTABILIDAD Y ASPECTOS ECONÃMICOS...	Dropbox\D8 RENTABILIDAD Y ASPECTOS ECONÃMICOS...	Dropbox\ICT Desarrollo\Variables Propuestas\D8...	Dropbox\ICT Desarrollo\Variables Propuestas\D8...	Dropbox\D9 PROMOCIÃN TURÃSTICA\NÃºmero de OI...	Dropbox\D9 PROMOCIÃN TURÃSTICA\NÃºmero de vi...	Dropbox\D9 PROMOCIÃN TURÃSTICA\Promedio de b...	Dropbox\D9 PROMOCIÃN TURÃSTICA\Presupuesto d...	Dropbox\D9 PROMOCIÃN TURÃSTICA\Presupuesto d...	Dropbox\ICT Desarrollo\Variables Propuestas\D9...	Dropbox\ICT Desarrollo\Variables Propuestas\D1...	Dropbox\ICT Desarrollo\Variables Propuestas\D1...	Dropbox\ICT Desarrollo\Variables Propuestas\D1...	Dropbox\ICT Desarrollo\Variables Propuestas\D1...	Dropbox\ICT Desarrollo\Variables Propuestas\D1...	NaN

	Regions	Density of restaurants per 100,000 habitants	People working at restaurants per 10,000 habitants	Car rental agencies	Hospital beds per 10,000 habitants	Density of ATM machines per 100,000 habitants	Spas	Casinos per 100,000 habitants	Golf courses	Artwork Centers	Density of tourist guides per 100,000 habitants	Thermal centers	Density of sports centers per 10,000 habitants	Ski resorts	Major shopping centers	Phone service penetration pero 1,000 habitants	Density of service stations	Number of certified tourism-operators	Number of vineyards	Percentage of crime perception	Percentage of homes with at least one crime victim per year	Density of murders per one million habitants	Density of public health complaints per one million habitants	Crime index	Allocated budget for public safety (millions of chilean pesos)	Porcentage of homes that reported at least one crime	Number of reported crimes	Number of crimes investigated and resolved	Number of accidents (roads; air; water)	Illegal commerce	Number of police units	Unemployment rate	Poverty rate	Industrial and Intellectual property crimes	Yearly strikes	Average length of strikes	Estimated daily cost per individual participating on a strike	Banks per one million habitants	Temporary residents	Exports (volume in million chilean pesos)	Density of tourism offices per one million habitants	Number of tourism information offices	Average number of internet searches for main tourist attractions	Yearly budget for tourism promotion ($M)	Yearly budget for international tourism promotion ($M)	Governmental resources allocated to tourism promotion ($M)	Public infrastructure investments related to the tourism sector ($)	Entrepreneurial and Innovation funds allocated to tourism related projects ($)	Seed funds allocated to the tourism sector ($)	Governmental funds devoted to tourism development ($)	Number of regional strategy plans for tourism development
0	Arica y Parinacota (XV)	35.857	19.563	12	21.619	43.766	0	5.273	1	20	20.038	5	0.000	-	-	240.55	17	6	-	0.318	0.260	42.184	15.819	0.513	11,884,613	0.426	26,467	2,387	981	319	265	7.3	8.2	84.369	1	11	253	94.915	45,560	137	5.273	6,517	1,060,490	193,364	118,413	92,641	36,370,835	11,757,543	446,922	24,729,573	1
1	TarapacÃ¡ (I)	25.947	26.616	2	18.958	60.264	0	4.185	3	9	22.180	5	40.134	-	2	213.07	28	0	-	0.421	0.468	87.884	0.000	0.516	14,740,693	0.398	76,896	1,862	1,105	593	592	4.8	13.4	485.457	6	14.9	59,745	138.104	83,265	3,236	4.185	7,600	1,750,481	74,404	13,600	-	42,364,100	12,796,789	355,208	29,714,382	2
2	Antofagasta (II)	37.248	35.446	22	29.960	69.233	3	4.049	3	36	20.446	2	14.292	-	3	259.23	39	2	-	0.546	0.314	109.315	0.000	0.381	22,383,612	0.495	123,017	4,228	2,324	7	765	6.2	7.3	12.146	13	9.6	30,220	178.143	238,010	16,666	4.049	16,280	2,846,340	200,000	149,000	149,744	38,262,875	29,066,423	192,572	50,841,622	1
3	Atacama (III)	44.823	26.933	19	25.478	56.225	1	3.932	1	9	18.479	3	32.437	-	-	184.01	35	1	-	0.390	0.197	66.841	0.000	0.488	17,068,077	0.533	21,705	3,125	965	14	504	6.4	10.2	15.727	3	29.3	17,860	145.477	53,046	3,456	3.932	11,614	556,668	94,100	187,035	-	35,948,639	16,986,593	233,225	30,224,699	2
4	Coquimbo (IV)	37.632	25.165	9	17.805	47.745	3	3.316	3	13	6.963	3	14.091	-	1	150.06	72	3	-	0.288	0.218	155.833	0.000	0.396	20,225,710	0.449	69,974	4,203	1,831	100	1,082	6.0	12.3	11.605	3	29.0	1,431	112.730	275,447	2,229	1.658	11,056	5,364,222	189,900	72,917	-	64,917,630	33,222,779	673,800	44,790,979	3
5	ValparaÃso (V)	52.408	47.050	16	27.756	55.850	9	1.948	12	44	12.209	3	18.710	2	8	230.65	179	3	5	0.311	0.291	87.671	1.948	0.432	24,137,492	0.445	236,177	19,176	6,765	166	2,761	7.5	11.6	50.654	10	10.7	16,641	143.520	1,557,887	4,287	1.299	5,649	3,058,423	129,481	63,996	1,079,295	101,802,434	24,239,428	811,772	51,647,041	1
6	Metropolitana (RM)	9.223	16.914	47	25.508	65.499	0	0.000	21	42	4.834	4	14.717	5	33	331.61	507	39	1	0.372	0.295	65.334	0.660	0.47	60,400,444	0.424	1,146,510	53,517	23,242	7,605	9,385	7.7	8.8	39.596	102	12.3	147,198	1295.113	7,307,884	6,196	0.330	19,352	13,709,951	56,982	-	-	230,562,951	43,552,270	62,052	90,875,903	1
7	O'Higgins (VI)	18.959	49.422	6	19.946	43.427	0	2.562	3	39	9.992	2	14.950	1	2	117.30	114	3	7	0.203	0.220	51.241	0.000	0.396	18,182,163	0.477	111,020	4,894	2,451	762	1,136	6.0	9.9	11.529	7	10.2	26,831	95.805	265,601	3,056	1.281	4,700	754,000	179,000	6,000	345,000	50,200,910	17,850,727	444,491	43,045,390	1
8	Maule (VII)	15.637	19.943	10	23.852	35.899	9	1.101	1	24	8.700	5	25.383	-	3	109.73	130	0	2	0.245	0.227	27.530	1.101	0.427	25,936,948	0.530	124,820	6,018	4,771	194	1,875	6.4	15.8	11.012	5	10.7	1,371	48.346	165,417	1,311	1.101	7,183	647,510	168,850	8,000	218,000	97,730,159	19,292,650	486,500	48,826,193	2
9	BiobÃo (VIII)	14.128	14.004	16	27.160	38.194	2	1.612	6	26	1.612	3	20.531	1	3	162.84	211	1	-	0.360	0.310	63.925	0.000	0.42	41,208,340	0.410	292,281	19,222	5,713	37	3,193	7.9	15.8	12.892	12	11.8	15,518	188.607	305,920	4,475	1.074	7,362	3,548,528	317,000	44,000	319,660	137,998,425	28,228,357	1,646,127	88,172,439	3
10	AraucanÃa (IX)	15.986	15.687	25	25.887	40.826	23	2.300	2	16	9.085	13	22.736	4	1	135.88	118	5	-	0.381	0.288	78.203	1.150	0.369	29,000,528	0.469	140,063	8,685	3,161	323	1,550	8.0	18.1	20.701	4	30.3	5,856	232.887	395,583	372	1.150	9,488	1,842,375	108,799	27,507	-	67,212,297	31,050,589	-	54,529,735	2
11	Los RÃos (XIV)	34.512	26.207	9	30.472	42.649	2	2.806	1	40	8.698	12	0.000	1	-	191.32	44	1	-	0.274	0.270	101.011	0.000	0.298	15,834,434	0.440	22,076	3,235	818	29	773	6.4	14.3	11.223	4	11.3	6,171	51.623	71,200	71	2.806	7,109	1,476,220	253,292	35,000	13,000	49,376,288	22,065,484	486,875	32,409,855	1
12	Los Lagos (X)	36.415	27.360	24	22.644	47.298	2	4.186	2	27	20.928	8	25.728	2	2	162.58	86	6	-	0.323	0.201	104.641	0.000	0.374	19,901,362	0.426	85,949	8,729	2,023	192	1,409	3.5	11.8	23.719	2	8	2,160	994.622	122,157	2,251	4.186	4,708	7,108,761	264,511	96,915	63,500	101,170,739	40,816,121	737,875	62,581,190	2
13	AysÃ©n (XI)	76.509	40.987	15	27.543	43.720	0	10.930	0	57	67.765	4	24.264	1	-	170.81	20	0	-	0.351	0.256	174.879	10.930	-	15,972,532	0.445	14,119	3,349	422	-	469	4.4	5.2	32.790	1	7	1,953	106.083	13,667	379	10.930	5,660	200,654	65,000	454,500	536,488	32,558,406	13,692,259	794,614	29,462,726	2
14	Magallanes y AntÃ¡rtica (XII)	59.008	59.340	12	34.676	71.606	0	13.260	1	15	137.244	0	18.366	1	2	297.25	21	3	-	0.283	0.180	53.041	6.630	0.367	20,075,842	0.414	8,659	2,527	660	-	216	5.4	6.1	86.192	1	22	616	5.115	322,800	397	13.260	4,217	558,678	266,000	525,000	32,100	30,955,753	18,036,736	357,752	30,288,105	1

	PC1	PC2	PC3	PC4	PC5	PC6
0	-3.484852	1.764464	-1.798993	0.024696	-3.293384	4.963699
1	-2.358607	1.929934	-5.155752	-0.639984	2.529265	4.532263
2	-0.861427	3.892164	-3.933696	0.021090	3.015185	-4.496842
3	-3.615715	1.076837	-5.142793	-1.089563	0.783786	-1.302723
4	-1.040964	-3.107735	-1.712781	2.985098	2.760371	-0.228396
5	4.835292	-1.893433	1.874994	10.991644	-0.205120	-0.509208
6	22.254643	4.440057	0.055366	-2.495396	-1.631285	0.907723
7	-1.245878	-4.403380	-1.678822	1.831481	-3.458413	0.772239
8	-1.216612	-5.416131	-1.031684	-1.725436	-2.242107	-0.352112
9	2.999536	-3.681077	-1.135120	-2.910789	1.849205	-3.578947
10	-0.380547	-5.981712	4.675446	-3.332380	-2.965237	-1.306414
11	-2.892189	-2.018186	0.855408	-2.756504	-0.023953	0.427947
12	-0.327681	-0.260155	6.978458	-1.146541	7.472138	2.638291
13	-6.865377	4.657334	4.155945	-0.800638	-2.001802	0.009124
14	-5.799622	9.001018	2.994022	1.043220	-2.588650	-2.476644

1 Data Preprocessing: chile_data1¶

First 5 dimensions:¶

01 CULTURAL HERITAGE AND EVENTS¶

02 NATURAL RESOURCES AND SUSTAINABILITY¶

04 TOURISM INFRASTRUCTURE¶

05 TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE¶

Now after replacement and transformation, we now have NaNs in 4 columns:¶

2 Data Preprocessing: chile_data2¶

Last 5 dimensions:¶

07 SECURITY AND SAFETY¶

08 ECONOMIC PERFORMANCE¶

09 TOURISM PROMOTION¶

10 GOVERNMENTAL INVOLVEMENT AND EFFICIENCY¶

Now after replacement and transformation, we now have NaNs in 8 columns:¶

3 Data Preprocessing: combine chile_data1 and chile_data2¶

4 Principal Component Analysis (PCA)¶

Eigenvalues and eigenvectors¶

According to the above result, we have 15 principal components here.¶

Thus, I will use only first 6 components, which explain 76.3% of variance.¶

5 Develop scoring system for 10 dimensions¶

5.1 - Calculate a weighted average for each variable in principal components.¶

5.2 - Calculate a score for each dimension.¶

Ranking for dimension 01 CULTURAL HERITAGE AND EVENTS¶

Ranking for dimension 02 NATURAL RESOURCES AND SUSTAINABILITY¶

Ranking for dimension 04 TOURISM INFRASTRUCTURE¶

Ranking for dimension 05 TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE¶

Ranking for dimension 07 SECURITY AND SAFETY¶

Ranking for dimension 08 ECONOMIC PERFORMANCE¶

Ranking for dimension 09 TOURISM PROMOTION¶

Ranking for dimension 10 GOVERNMENTAL INVOLVEMENT AND EFFICIENCY¶

5.3 - Create an aggregated dataframe with all scores¶

6 Conclusion Outline¶

Extra research done:¶

Suggestion Overview¶

7 Table manipulation for Taleau visualization¶

8 Appendix - Relevant Visualization & Slides¶

	1	2	3	4	5	6	weighted_average
0
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR	-0.00349703	-0.0191299	-0.00471924	0.00557629	-0.0108352	0.00462587	-0.036691
NUMBER OF CULTURAL CENTERS	0.0461821	-0.0037429	0.00451749	0.00276272	0.00216259	-0.00201985	0.065388
WORLD CULTURAL HERITAGE SITES	0.00489752	-0.00556496	0.00332327	0.0188002	0.00523406	0.00590969	0.042750
NUMBER OF ARCHEOLOGICAL SITES	-0.00462714	0.00577805	-0.0110068	0.0127451	-0.00122614	0.00293163	0.006025
NATIONAL MONUMENTS	0.0485681	0.00495675	0.00113125	0.0013392	-0.00214279	0.000559138	0.071354

	CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR	NUMBER OF CULTURAL CENTERS	WORLD CULTURAL HERITAGE SITES	NUMBER OF ARCHEOLOGICAL SITES	NATIONAL MONUMENTS	MUSEUMS	% OF POPULATION THAT ATTENDS MUSEUMS	THEATERS	NUMBER OF THEATER PLAYS PER YEAR	LIBRARIES	GALERIES	% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP	NUMBER OF EXHIBITS	ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR	MAJOR SPORTS EVENTS PER YEAR	OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS	ARTWORK SITES	POPULAR ARCHITECTURE SITES	HISTORICAL SITES	LOCAL MARKETS	CULTURAL SITES LEVEL III (INTERNATIONAL)	CULTURA SITES LEVEL II (NATIONAL)	CULTURAL SITES LEVEL I (LOCAL)	HERITAGE ARCHITECTURAL HOUSES	Ranking 1
Regions
Metropolitana	0.022604	0.178081	-0.023898	-0.004221	0.248680	0.177626	0.024468	0.215574	0.228102	0.167096	0.225741	0.004004	0.218395	0.027074	0.01006	0.022794	0.014717	-0.017975	-0.001167	0.006126	-0.003683	0.178952	0.196274	0.047414	2.162837
ValparaÃso	-0.008502	0.093351	0.119491	0.009263	0.059484	0.014645	0.056125	-0.010277	-0.012214	0.039436	0.035865	0.004462	-0.006097	-0.021659	-0.01509	0.037041	0.014717	0.017372	0.012842	0.006126	0.162059	0.196919	0.017985	0.186658	1.010002
BiobÃo	0.019493	0.071054	-0.023898	-0.004807	-0.019932	0.071334	-0.011146	0.007648	-0.005816	0.052023	0.035865	0.004106	0.052104	0.027074	0.01006	-0.019945	-0.002943	-0.011399	0.015177	-0.000942	-0.019468	-0.090554	0.060212	0.004017	0.219315
Los Lagos	0.031936	0.035378	0.047796	-0.004807	-0.016039	0.007559	-0.002242	-0.006692	-0.014140	-0.005514	-0.027426	-0.004482	-0.014412	0.027074	0.01006	-0.019945	0.032377	0.009152	-0.008172	0.002592	-0.019468	0.078336	0.003910	-0.008783	0.134046
Antofagasta	0.031936	-0.031513	-0.023898	0.011608	-0.002803	0.042989	0.072942	-0.006692	-0.016799	-0.034282	-0.016878	0.003191	-0.014412	0.010830	0.01006	0.022794	0.023547	-0.002356	0.031521	-0.008011	-0.003683	-0.015092	-0.005474	-0.018462	0.061062

	% OF LAND THAT CORRESPONDS TO FORESTS	NATIONAL PROTECTED SITES (%)	% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS	TOXIC WASTE DISPOSAL (TONS/100 hab.)	NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED	ENVIRONMENTAL ISSUES PER MILLION HABITANTS	NUMBER OF BEACHES AND BEACH RESORTS	LAND AFFECTED BY WILDFIRES	NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)	NATURAL PROTECTED SITES LEVEL II (NATIONAL)	RIVERS, LAKES AND WATERFALLS	MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS	GEISERS AND THERMAL CENTERS	PIERS AND SEASHORES	GLACIERS AND WINTER VACATION LOCATIONS	VALLEYS	DESERTS AND DUNES	ISLANDS AND PENINSULAS	PALEONTOLOGY SITES	HIKING TRAILS	PRESERVED SITES	SEASHORE PROTECTED SITES	BIOSHPERE RESERVES	Ranking 2
Regions
ValparaÃso	-0.003891	-0.003753	0.045796	-0.003377	0.124220	0.01448	0.021560	0.085375	0.005407	0.004051	0.025737	0.036527	0.009353	0.012488	-0.000529	0.081704	0.011421	-0.019645	0.035408	-0.029757	-0.014996	0.112576	0.051250	0.601401
Metropolitana	-0.003744	-0.004506	0.240547	0.010396	0.164148	0.01448	-0.009165	-0.011687	-0.007266	-0.003461	0.033357	0.018058	0.005661	-0.015411	-0.000048	0.005473	0.019207	-0.019645	0.035408	0.013942	-0.014996	-0.032164	-0.025625	0.412958
Los Lagos	0.014507	0.001768	-0.027331	-0.009308	-0.035492	0.01448	0.000208	-0.012275	0.004140	0.000736	-0.009821	-0.025035	-0.012799	0.004517	-0.000529	-0.012119	0.019207	0.116699	0.035408	0.013942	0.114972	0.112576	0.012813	0.321265
Coquimbo	-0.005997	-0.004715	-0.020890	-0.024287	-0.035492	0.01448	0.010103	0.009785	-0.005999	-0.002025	0.015578	0.036527	0.016737	0.026438	-0.001251	0.058248	0.019207	0.019939	0.015737	0.013942	-0.014996	0.064329	0.012813	0.218210
Magallanes y AntÃ¡rtica	0.001170	0.019081	-0.032635	0.000030	-0.008873	0.01448	-0.009165	-0.012128	0.013012	0.003056	-0.025060	-0.025035	0.020428	0.018467	0.003560	0.011337	0.019207	-0.019645	0.015737	0.010821	-0.014996	0.016082	0.012813	0.031743

	% AVAILABLE WORKFORCE	% POPULATION ORIENTED TOWARDS TOURISM	AVERAGE MONTHLY INCOME (CHILEAN PESOS)	5 POPULATION WITH PRIMARY EDUCATION	% POPULATION WITH SECONDARY EDUCATION	AVERAGE NUMBER OF YEARS STUDYING	HIGHER EDUCATION AND TECHNICAL INSTITUTIONS	TOURISM-ORIENTED INSTITUTIONS	NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS	AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS	DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)	CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS	Ranking 3
Regions
Metropolitana	0.011563	0.063621	0.022020	0.000036	0.000092	0.076338	0.018410	0.202143	0.237468	0.243001	-0.003302	0.235662	1.107052
ValparaÃso	0.000380	-0.020233	-0.011800	0.000391	-0.000506	0.038588	0.010318	0.116328	0.047224	0.034620	-0.002071	0.025286	0.238526
Los Lagos	0.005204	0.210986	-0.017885	0.000391	0.000849	-0.055785	0.006272	0.011442	-0.018654	-0.019184	-0.000615	0.007288	0.130308
BiobÃo	-0.022688	-0.016264	-0.018212	0.000746	-0.000028	-0.005453	0.014364	0.078188	0.048801	0.035502	-0.003840	0.001137	0.112252
TarapacÃ¡	0.071381	-0.012625	0.009741	0.002522	0.000849	0.044880	-0.004855	-0.036233	-0.031455	-0.030871	-0.000406	-0.034423	-0.021496

	% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR	ROOMS PER 1000 HABITANTS	NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.	TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)	AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR	AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND	NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION	NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS	TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS	TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)	Ranking 4
Regions
Metropolitana	0.132766	-0.007908	0.150957	-0.007816	0.107389	-0.001257	0.068811	0.255904	0.154317	0.024250	0.877413
ValparaÃso	-0.006070	-0.002575	0.156550	-0.001094	-0.024942	0.000539	0.059677	0.022329	0.196628	0.027814	0.428856
Los Lagos	-0.026843	0.003653	0.098968	0.000402	-0.016589	0.002335	0.178422	-0.008520	-0.024342	-0.007661	0.199826
Coquimbo	-0.017419	-0.001256	0.026945	-0.006297	-0.000762	-0.006645	-0.031665	-0.017335	0.059820	0.111495	0.116883
AraucanÃa	-0.045889	-0.004629	0.038480	-0.003979	0.008910	-0.003951	0.068811	-0.021742	-0.032023	-0.011565	-0.007576

	NATIONAL TOURISTS ARRIVALS	INTERNATIONAL TOURISTS ARRIVALS	NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY	DENSITY OF AIRPORTS	DENSITY OF ROADS AND HIGHWAYS	% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)	NUMBER OF VEHICLES	VISITORS TO PROTECTED SITES	NUMBER OF CRUISES THAT ARRIVE PER YEAR	TOURIST'S ARRIVALS THROUGH BORDER LINES	SECONDARY ROADS (KMS)	NUMBER OF INTERNATIONAL BORDER GATES	Ranking 5
Regions
Metropolitana	0.091085	0.270813	0.127377	-0.005321	0.035771	0.099077	0.231574	-0.010282	-0.033870	0.259068	0.003948	0.002762	1.072002
Los Lagos	0.021159	0.004749	0.002827	0.140180	0.007550	-0.007560	-0.015696	0.099796	0.112783	-0.003210	0.033731	-0.001841	0.394468
ValparaÃso	0.083664	0.005501	0.011497	-0.014845	0.048740	0.030663	0.023559	0.006833	0.128496	0.063652	-0.007240	0.002762	0.383284
Arica y Parinacota	-0.050274	-0.023790	0.136431	0.054877	0.001818	-0.038599	-0.033488	-0.028957	0.034219	-0.010293	-0.013692	-0.001841	0.026411
BiobÃo	0.068799	-0.024908	-0.035789	-0.024638	0.028728	0.062178	0.022606	-0.019459	-0.033870	-0.056033	0.032436	0.002762	0.022813

	Density of restaurants per 100,000 habitants	People working at restaurants per 10,000 habitants	Car rental agencies	Hospital beds per 10,000 habitants	Density of ATM machines per 100,000 habitants	Spas	Casinos per 100,000 habitants	Golf courses	Artwork Centers	Density of tourist guides per 100,000 habitants	Thermal centers	Density of sports centers per 10,000 habitants	Ski resorts	Major shopping centers	Phone service penetration pero 1,000 habitants	Density of service stations	Number of certified tourism-operators	Number of vineyards	Ranking 6
Regions
Metropolitana	-0.013683	-0.011128	0.174597	0.000161	0.064375	0.013533	0.005582	0.233964	0.032093	-0.003302	0.003207	0.000941	0.142849	0.256241	0.134303	0.185763	0.249066	0.000000	1.468560
ValparaÃso	0.009894	0.014416	-0.001515	0.001782	0.022078	-0.020300	0.002928	0.110101	0.036613	-0.002071	0.007215	0.000082	0.030073	0.035344	0.033479	0.033030	-0.013621	0.011108	0.310636
Magallanes y AntÃ¡rtica	0.013497	0.024834	-0.024239	0.006773	0.091145	0.013533	-0.012482	-0.041288	-0.028929	0.018807	0.019240	0.000156	-0.007518	-0.017672	0.099990	-0.040543	-0.013621	-0.002777	0.098906
Antofagasta	0.001617	0.004580	0.032571	0.003372	0.080743	0.002256	0.000066	-0.013763	0.018532	-0.000696	0.011223	0.001032	-0.045110	-0.008836	0.062021	-0.032161	-0.020918	-0.002777	0.093754
Los Lagos	0.001162	-0.002274	0.043933	-0.001904	-0.015410	0.006015	-0.000121	-0.027525	-0.001808	-0.000615	-0.012826	-0.001429	0.030073	-0.017672	-0.034500	-0.010275	0.008270	-0.002777	-0.039682

	Percentage of crime perception	Percentage of homes with at least one crime victim per year	Density of murders per one million habitants	Density of public health complaints per one million habitants	Crime index	Allocated budget for public safety (millions of chilean pesos)	Porcentage of homes that reported at least one crime	Number of reported crimes	Number of crimes investigated and resolved	Number of accidents (roads; air; water)	Illegal commerce	Number of police units	Ranking 7
Regions
Metropolitana	0.004609	0.003871	-0.008142	0.000154	0.013426	0.136211	0.035714	0.224131	0.229828	0.220147	0.232138	0.214621	1.306709
BiobÃo	0.002995	0.005896	-0.008736	0.000208	0.000604	0.064792	0.053529	0.028737	0.050038	0.021503	-0.025856	0.040980	0.234692
ValparaÃso	-0.003596	0.003331	0.001280	0.000049	0.003682	0.001268	0.008992	0.015904	0.049797	0.033425	-0.021458	0.028865	0.121539
TarapacÃ¡	0.011199	0.027231	0.001370	0.000208	0.025222	-0.033700	0.068799	-0.020530	-0.040970	-0.030716	-0.006902	-0.031959	-0.030747
AraucanÃa	0.005819	0.002926	-0.002713	0.000114	-0.012474	0.019364	-0.021547	-0.006081	-0.005201	-0.007417	-0.016106	-0.005094	-0.048411

	Unemployment rate	Poverty rate	Industrial and Intellectual property crimes	Yearly strikes	Average length of strikes	Estimated daily cost per individual participating on a strike	Banks per one million habitants	Temporary residents	Exports (volume in million chilean pesos)	Ranking 8
Regions
Metropolitana	-0.012601	0.022016	-0.001791	0.229046	0.010567	0.192215	0.221929	0.255024	0.012551	0.928958
Los Lagos	0.024152	-0.004906	-0.003142	-0.024324	0.026200	-0.030914	0.157788	-0.024340	-0.004169	0.116346
Antofagasta	0.000525	0.035477	-0.004127	0.003547	0.020383	0.012254	-0.016492	-0.019836	0.056926	0.088657
TarapacÃ¡	0.012776	-0.019264	0.036150	-0.014189	0.001115	0.057675	-0.025039	-0.025852	0.000006	0.023379
ValparaÃso	-0.010851	-0.003111	-0.000850	-0.004054	0.016384	-0.008637	-0.023882	0.031478	0.004460	0.000938

	Density of tourism offices per one million habitants	Number of tourism information offices	Average number of internet searches for main tourist attractions	Yearly budget for tourism promotion ($M)	Yearly budget for international tourism promotion ($M)	Governmental resources allocated to tourism promotion ($M)	Ranking 9
Regions
Metropolitana	0.000148	0.062659	0.244132	0.024464	-1.273211e-18	0.000000	0.331403
ValparaÃso	0.000106	-0.016948	0.002110	0.008869	-5.661756e-03	0.086832	0.075307
Coquimbo	0.000091	0.014464	0.054502	-0.004127	-4.881219e-03	0.000000	0.060048
Los Lagos	-0.000018	-0.022415	0.094141	-0.020177	-2.781528e-03	-0.024206	0.024544
Antofagasta	-0.000012	0.044812	-0.002709	-0.006300	1.775618e-03	-0.014779	0.022788

	Ranking 1	Ranking 2	Ranking 3	Ranking 4	Ranking 5	Ranking 6	Ranking 7	Ranking 8	Ranking 9	Ranking 10	Overall
Regions
Arica y Parinacota	-0.386865	-0.011767	-0.206847	-0.146438	0.026411	-0.184242	-0.168982	-0.099796	-0.082051	-0.136136	-1.396713
TarapacÃ¡	-0.161739	-0.099703	-0.021496	-0.070096	-0.116496	-0.222647	-0.030747	0.023379	-0.022595	-0.148637	-0.870778
Antofagasta	0.061062	-0.243534	-0.042198	-0.018416	-0.053621	0.093754	-0.133656	0.088657	0.022788	0.032808	-0.192355
Atacama	-0.344736	-0.359092	-0.175224	-0.191659	-0.345047	-0.186519	-0.280487	-0.124936	-0.015453	-0.126136	-2.149288
Coquimbo	-0.182362	0.218210	-0.110381	0.116883	-0.172357	-0.252794	-0.113139	-0.168360	0.060048	-0.020128	-0.624382
ValparaÃso	1.010002	0.601401	0.238526	0.428856	0.383284	0.310636	0.121539	0.000938	0.075307	0.058180	3.228670
Metropolitana	2.162837	0.412958	1.107052	0.877413	1.072002	1.468560	1.306709	0.928958	0.331403	0.429481	10.097374
O'Higgins	-0.375904	-0.080616	-0.123246	-0.267996	-0.239521	-0.148752	-0.167534	-0.029760	-0.078559	-0.044798	-1.556685
Maule	-0.471167	-0.137135	-0.232702	-0.221008	-0.195883	-0.355407	-0.164965	-0.153725	-0.078070	-0.010065	-2.020127
BiobÃo	0.219315	-0.271272	0.112252	-0.100968	0.022813	-0.077813	0.234692	-0.082379	-0.028718	0.095221	0.123144
AraucanÃa	-0.509818	-0.232055	-0.165244	-0.007576	-0.067060	-0.156404	-0.048411	-0.210074	-0.015590	0.034195	-1.378035
Los RÃos	-0.370583	-0.110449	-0.244383	-0.105095	-0.264168	-0.219291	-0.199740	-0.145796	-0.097954	-0.045339	-1.802799
Los Lagos	0.134046	0.321265	0.130308	0.199826	0.394468	-0.039682	-0.068038	0.116346	0.024544	0.141728	1.354810
AysÃ©n	-0.514235	-0.039956	-0.182868	-0.352108	-0.401963	-0.128305	-0.124283	-0.032560	-0.001274	-0.169971	-1.947522
Magallanes y AntÃ¡rtica	-0.269854	0.031743	-0.083550	-0.141617	-0.042863	0.098906	-0.162958	-0.110890	-0.093827	-0.090402	-0.865313

	Ranking 1	Ranking 2	Ranking 3	Ranking 4	Ranking 5	Ranking 6	Ranking 7	Ranking 8	Ranking 9	Ranking 10	Overall
Regions
Arica y Parinacota	-0.386865	-0.011767	-0.206847	-0.146438	0.026411	-0.184242	-0.168982	-0.099796	-0.082051	-0.136136	-1.396713
TarapacÃ¡	-0.161739	-0.099703	-0.021496	-0.070096	-0.116496	-0.222647	-0.030747	0.023379	-0.022595	-0.148637	-0.870778
Antofagasta	0.061062	-0.243534	-0.042198	-0.018416	-0.053621	0.093754	-0.133656	0.088657	0.022788	0.032808	-0.192355
Atacama	-0.344736	-0.359092	-0.175224	-0.191659	-0.345047	-0.186519	-0.280487	-0.124936	-0.015453	-0.126136	-2.149288
Coquimbo	-0.182362	0.218210	-0.110381	0.116883	-0.172357	-0.252794	-0.113139	-0.168360	0.060048	-0.020128	-0.624382
ValparaÃso	1.010002	0.601401	0.238526	0.428856	0.383284	0.310636	0.121539	0.000938	0.075307	0.058180	3.228670
Metropolitana	2.162837	0.412958	1.107052	0.877413	1.072002	1.468560	1.306709	0.928958	0.331403	0.429481	10.097374
O'Higgins	-0.375904	-0.080616	-0.123246	-0.267996	-0.239521	-0.148752	-0.167534	-0.029760	-0.078559	-0.044798	-1.556685
Maule	-0.471167	-0.137135	-0.232702	-0.221008	-0.195883	-0.355407	-0.164965	-0.153725	-0.078070	-0.010065	-2.020127
BiobÃo	0.219315	-0.271272	0.112252	-0.100968	0.022813	-0.077813	0.234692	-0.082379	-0.028718	0.095221	0.123144
AraucanÃa	-0.509818	-0.232055	-0.165244	-0.007576	-0.067060	-0.156404	-0.048411	-0.210074	-0.015590	0.034195	-1.378035
Los RÃos	-0.370583	-0.110449	-0.244383	-0.105095	-0.264168	-0.219291	-0.199740	-0.145796	-0.097954	-0.045339	-1.802799
Los Lagos	0.134046	0.321265	0.130308	0.199826	0.394468	-0.039682	-0.068038	0.116346	0.024544	0.141728	1.354810
AysÃ©n	-0.514235	-0.039956	-0.182868	-0.352108	-0.401963	-0.128305	-0.124283	-0.032560	-0.001274	-0.169971	-1.947522
Magallanes y AntÃ¡rtica	-0.269854	0.031743	-0.083550	-0.141617	-0.042863	0.098906	-0.162958	-0.110890	-0.093827	-0.090402	-0.865313

	Ranking 1	Ranking 2	Ranking 3	Ranking 4	Ranking 5	Ranking 6	Ranking 7	Ranking 8	Ranking 9	Ranking 10	Overall	Regions
0	4.757803	36.161045	2.777496	16.727616	29.062671	9.384242	7.025266	9.681660	3.704004	5.644414	6.145147	Arica y Parinacota
1	13.167193	27.005775	16.492626	22.936694	19.367289	7.278663	15.734641	20.495674	17.551640	3.558959	10.439667	Tarapacá
2	21.489789	12.031106	14.960823	27.139950	23.632954	24.625476	9.250927	26.226744	28.121598	33.827487	15.979317	Antofagasta
3	6.331503	0.000000	5.117486	13.049664	3.861426	9.259357	0.000000	7.474529	19.215098	7.312544	0.000000	Atacama
4	12.396846	60.104715	9.915576	38.144147	15.577416	5.625795	10.543590	3.662179	36.799621	24.996641	12.451608	Coquimbo
5	56.936725	100.000000	35.733072	63.517737	53.274477	36.516146	25.329299	18.525508	40.353694	38.059925	43.913663	Valparaíso
6	100.000000	80.380548	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	Santiago
7	5.167239	28.992997	8.963640	6.840994	11.020767	11.329996	7.116483	15.830420	4.517309	20.881194	4.838895	Libertador General Bernardo O'Higgins
8	1.608772	23.108634	0.864384	10.662666	13.981323	0.000000	7.278316	4.947026	4.631139	26.675328	1.054662	Maule
9	27.401210	9.143218	26.389409	20.425791	28.818565	15.219239	32.458423	11.210803	16.125410	44.239148	18.555520	Bío-Bío
10	0.164990	13.226211	5.855973	28.021584	22.721250	10.910451	14.621760	0.000000	19.183125	34.058807	6.297660	La Araucanía
11	5.365983	25.886976	0.000000	20.090130	9.348616	7.462612	5.087365	5.643186	0.000000	20.791045	2.829255	Los Ríos
12	24.216044	70.834109	27.725421	44.890117	54.033228	17.309792	13.385137	28.657613	28.530523	51.997295	28.612678	Los Lagos
13	0.000000	33.226230	4.551884	0.000000	0.000000	12.451004	9.841475	15.584632	22.517324	0.000000	1.647522	Aysen del Gen. Carlos Ibáñez del Campo
14	9.128651	40.691009	11.900932	17.119681	24.362865	24.907971	7.404768	8.707682	0.961354	13.273586	10.484287	Magallanes

	Public infrastructure investments related to the tourism sector ($)	Entrepreneurial and Innovation funds allocated to tourism related projects ($)	Seed funds allocated to the tourism sector ($)	Governmental funds devoted to tourism development ($)	Number of regional strategy plans for tourism development	Ranking 10
Regions
Metropolitana	0.179054	0.113984	0.016517	0.093697	0.026229	0.429481
Los Lagos	0.030604	0.097888	-0.006260	0.032611	-0.013115	0.141728
BiobÃo	0.072856	0.023834	-0.036870	0.087860	-0.052459	0.095221
ValparaÃso	0.031329	0.000367	-0.008751	0.009005	0.026229	0.058180
AraucanÃa	-0.008356	0.040437	-0.000000	0.015228	-0.013115	0.034195

1 Data Preprocessing: chile_data1¶

First 5 dimensions:¶

01 CULTURAL HERITAGE AND EVENTS¶

02 NATURAL RESOURCES AND SUSTAINABILITY¶

03 HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT¶

04 TOURISM INFRASTRUCTURE¶

05 TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE¶

Now after replacement and transformation, we now have NaNs in 4 columns:¶

2 Data Preprocessing: chile_data2¶

Last 5 dimensions:¶

06 TOURISM-RELATED SERVICES¶

07 SECURITY AND SAFETY¶

08 ECONOMIC PERFORMANCE¶

09 TOURISM PROMOTION¶

10 GOVERNMENTAL INVOLVEMENT AND EFFICIENCY¶

Now after replacement and transformation, we now have NaNs in 8 columns:¶

3 Data Preprocessing: combine chile_data1 and chile_data2¶

4 Principal Component Analysis (PCA)¶

Eigenvalues and eigenvectors¶

According to the above result, we have 15 principal components here.¶

Thus, I will use only first 6 components, which explain 76.3% of variance.¶

5 Develop scoring system for 10 dimensions¶

5.1 - Calculate a weighted average for each variable in principal components.¶

5.2 - Calculate a score for each dimension.¶

Ranking for dimension 01 CULTURAL HERITAGE AND EVENTS¶

Ranking for dimension 02 NATURAL RESOURCES AND SUSTAINABILITY¶

Ranking for dimension 03 HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT¶

Ranking for dimension 04 TOURISM INFRASTRUCTURE¶

Ranking for dimension 05 TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE¶

Ranking for dimension 06 TOURISM-RELATED SERVICES¶

Ranking for dimension 07 SECURITY AND SAFETY¶

Ranking for dimension 08 ECONOMIC PERFORMANCE¶

Ranking for dimension 09 TOURISM PROMOTION¶

Ranking for dimension 10 GOVERNMENTAL INVOLVEMENT AND EFFICIENCY¶

5.3 - Create an aggregated dataframe with all scores¶

6 Conclusion Outline¶

Extra research done:¶

Suggestion Overview¶

7 Table manipulation for Taleau visualization¶

8 Appendix - Relevant Visualization & Slides¶